GlobModel

1
GlobModel

• The GlobModel study, initial findings and
  objectives of the day
• Zofia Stott
• 13 September 2007

2
Objective of presentation/contents

• Background to the GlobModel study
• Preliminary conclusions of the study
• Objectives of the day

3
Background to the GlobModel study

• EO data-model fusion is a relatively new area for
  ESA
• DUE Glob-projects
• Summer schools
• Ad hoc collaborations, eg with ECMWF
• Fact finding
• Programmes, initiatives, organisations, people
• European focus
• Also international programmes, eg IGBP, WCRP
• Analogies with US where appropriate
• Opinion seeking
• What are the issues for the European community?
• Strategy and implementation plan for ESA
• Where should ESA be involved?
• How should ESA be involved?

AnalysisReport
Workshop
4
Background to the GlobModel study

• Scope
• Numerical Weather Prediction
• Re-analysis
• New (pre)-operational services, eg GMES Fast
  Track services
• Ocean forecasting
• Chemical weather forecasting
• Global change and Earth system science
• EO data-model fusion
• Data assimilation
• Ancillary surface data fields
• Model validation

5
Background to the GlobModel study

• GlobModel hypothesis
• Understanding, forecasting and predicting the
  behaviour of the Earth system depends on
• Data and models working together
• Satellite data are key
• Progress is accelerated by collaboration between
  the science base and operational services
• Objective is to create a virtuous circle
• High scientific return
• Linked to new operational services
• Leading to investment in both new research and
  operational missions

6
Background to the GlobModel study

• Specific requirements/issues
• The role of OSSE and OSE in quantifying the
  impact of particular data streams
• Concerns about data continuity over the next 10
  years
• Areas where new or improved instruments are
  required
• Novel data products specifically tailored for
  model assimilation (eg radiances V retrievals V
  gridded fields)
• Improved techniques for EO data-model fusion (eg
  development of new data assimilation techniques,
  observation operators)
• Intercomparison and cross validation of different
  data sets
• Improved model development environments which
  include consideration of EO data issues
• Standardisation and harmonisation of EO data
  formats, data discovery and data access
• Improved quality control
• Software tools to support the use of EO data
  streams
• Real time delivery and long term curation
• Provision of high level products, eg model
  independent reanalyses
• Shared high performance computing environment
• Training.

7
Preliminary recommendations OSE, OSSE
The Global Observing System, Jean-Noël Thépaut,
Data Assimilation Training Course, ECMWF
Reading, 25 April- 4 May 2007
8
Preliminary recommendations access to
operational systems

• Make operational systems more readily available
  for research
• Mutual benefit
• Scientists work on topics of interest to
  operational agencies
• Benefit from operational facilities (models,
  computer resources, expert help)
• Operational agencies benefit from latest research
  results
• Increases chances to technology transfer from
  research base to operations

9
Preliminary recommendations integrated data
systems

• Increase emphasis on integrated data systems for
  new services
• Optimise in situ and satellite components
• Eg What is the balance between Argo floats and
  altimeters?
• GODAE/GHRSST/Medspiration projects optimising sea
  surface temperature retrievals could be taken as
  an example of good practice

10
Preliminary recommendations

• Develop observation operators
• Fundamental link between data and models
• Essential to ensure early take up of data into
  operational systems
• Commit to long term continuity of re-analysis
• Develop the use of EO data in the land and
  cryosphere components of the Earth system models
• Develop climate quality data sets

11
Preliminary recommendations - people

• Ensure that the right mix of people/institutions
  are brought together
• Experts on satellite data processing, retrievals
• Experts on operational data assimilation systems
• Experts on Earth system modelling in the research
  community
• Members of satellite instrument and/or science
  teams
• Participants in the cal-val effort
• Members of the satellite data management teams.

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Preliminary conclusions provide a science focus

• Address the big science issues
• Develop regional climate models able to identify
  tipping points in the climate system
• Understand link between physical and biological
  feedbacks in carbon cycle
• Understand links between climate change and
  atmospheric composition
• Develop coupled sea-ice and ocean circulation
  models
• Develop improved ability to model hydrological
  cycle and predict high impact weather
• Develop ecosystem and biodiversity models

13
Objectives of the day - Splinter sessions

• Where are we today?
• What are the key issues?
• What is your vision for Earth system modelling in
  10 years time?
• What will we be able to do which we cannot do
  today? Eg
• Forecast on an annual/decadal and regional basis?
• Forecast high impact weather?
• Identify and monitor all climate tipping points?
• What role should EO play in achieving our goals?
• What programmes and projects would you recommend
  to ESA to fulfil your objectives?

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Backup slides
15
NWP I

• Developments driven by operational requirements
  of forecasting centres
• New services
• Seasonal and inter annual forecasts
• High impact weather
• New and improved services, based on
• Better models
• Better data
• Satellite data are key
• Innovation needs close links between RD and
  operations

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NWP II

• Pull through of satellite data for NWP, in Europe
• Strong for meteorological data sources
• Eg via EUMETSAT SAFs
• Weaker for non EUMETSAT data
• Ad hoc
• But good examples of transfer from research to
  operational status eg scatterometer, GOME,
  altimetry
• Key satellite requirements
• Low level (1B/C) radiances
• Some retrievals (eg Atmospheric Motion Vectors)
• Surface gridded fields
• Real time delivery (lt1 hour)
• BUFR, GRIB
• High priority issues
• Improved coupled models
• Use of satellite radiances over land, cloud
• Hydrological cycle
• Improved surface representation/assimilation

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NWP III

• Increasing experience of OSE, OSSE
• Quantify impact of satellite data on NWP
• Comparison of Europe with USA
• JCSDA
• NASA/NOAA initiative
• To accelerate take up of new data sources

18
NWP IV
The Global Observing System, Jean-Noël Thépaut,
Data Assimilation Training Course, ECMWF
Reading, 25 April- 4 May 2007
19
NWP V
The Global Observing System, Jean-Noël Thépaut,
Data Assimilation Training Course, ECMWF
Reading, 25 April- 4 May 2007
20
NWP VI

• Messages from NWP
• NWP key for operational data assimilation
• 40 years of infrastructure and capability
• Need to work effectively with NWP centres
• EUMETSAT, ECMWF, national met offices
• No equivalent of GMAO or JCSDA in Europe
• No systematic mechanisms for accelerating
  transfer of research data sources to operations
• ADM, SMOS already identified by ECMWF

21
Reanalysis I

• Long term (eg 40 years) global data sets of past
  climate using data assimilation
• Reliant of latest NWP model historical data
• ECMWF leads in Europe
• Key for
• Understanding climate trends
• Improving both models and data (biases)
• Challenges
• Need for improved coupled models
• Inhomogeneities in data records

22
Reanalysis II

• Messages from reanalysis
• Long term missions needed
• Repeats
• Overlaps
• Long term curation of data a major challenge
• European reanalysis projects are
• Add on to existing activities, not core
  business
• Funding ad hoc
• No sustained European effort in reanalysis

23
New (pre)-operational forecasting I

• Ocean forecasting
• Chemical weather forecasting
• Learning from current practice in NWP
• Reliant on NWP either through loosely or tightly
  coupled models
• GMES Core Services providing a European delivery
  structure
• Far less technically mature than NWP
• Requirements less precise
• Techniques more experimental

24
New (pre)-operational forecasting II

• Data types
• Ocean forecasts
• Broad correspondence between GMES Sentinel 3 and
  ocean forecasts (altimetry, SST, ocean colour)
• Also ocean salinity (SMOS), sea ice thickness
  (Cryosat), gravity/geoid (GRACE/GOCE), wind/waves
  (scatterometer)
• Chemical weather forecasting
• Broad correspondence between GMES Sentinels 4/5
  and chemical weather forecasting
• Also METOP, MSG, ENVISAT, AURA instruments
• PLUS NWP outputs (forcing fields)

25
New (pre)-operational forecasting III

• Messages
• Continued development through close
  research/operational interactions
• Models immature in key areas of user interests,
  eg
• boundary layer chemical forecasts
• coupled physical-biogeochemical models and
  assimilation of ocean colour data
• Need for better comparison between data and
  models
• Standards, data formats are still evolving etc
• GMES and INSPIRE are addressing this
• Tools, training, common research hub to exchange
  data and models
• Important to work with emerging structures
• Eg EUROGOOS for ocean forecasting

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Earth system science I

• Developing GCMs
• Whats new
• Shorter timescales (from centuries to decades),
  more local impacts (from global to regional)
• Representation of energy and hydrological cycle
• Ocean variability and climate change signals
• Developing land surface models in GCMs
• Developing models of coupled atmosphere/
  ocean/cryosphere

27
Earth system science II

• Global carbon cycle
• Quantifying surface fluxes
• Quantifying role played by fire
• Identifying weights of key processes in tropics
  for post-Kyoto negotiations
• Atmospheric composition
• Understanding interactions between climate change
  and atmospheric composition
• Cryosphere
• Strongest signals of climate change, but key
  processes poorly represented in models
• Predictability of high impact weather
• Monitoring, understanding, predicting behaviour
  of ecosystems
• Impacts of natural resource depletion