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GlobModel

The GlobModel study, initial findings and objectives of the day

Zofia Stott

13 September 2007

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Objective of presentation/contents

Background to the GlobModel study

Preliminary conclusions of the study

Objectives of the day

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Background to the GlobModel studyEO data-model fusion is a relatively new area for ESA

DUE Glob-projectsSummer schoolsAd hoc collaborations, eg with ECMWF

Fact findingProgrammes, initiatives, organisations, people

• European focus • Also international programmes, eg IGBP, WCRP• Analogies with US where appropriate

Opinion seekingWhat are the issues for the European community?

Strategy and implementation plan for ESAWhere should ESA be involved?How should ESA be involved?

AnalysisReport

Workshop

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Background to the GlobModel study

ScopeNumerical 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 fusionData assimilation

Ancillary surface data fields

Model validation

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Background to the GlobModel study

GlobModel hypothesisUnderstanding, 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 servicesObjective is to create a “virtuous circle”

• High scientific return • Linked to new operational services• Leading to investment in both new research and

operational missions

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Background to the GlobModel studySpecific 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.

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Instruments have been ranked:Geopotential 500 hPa Southern Hemisphere

SH RMS error 500 hPa geopotential

020406080

100120

Day2 Day5 Day7

(m)

controlamsua

airshirs

scatssmi

amsubcsr

geo amvreference

baseline

AIRS (1 instrument) and AMSU-A (3-4 instruments) constellation clearly emerge from the pack

Control:all

Baseline:conv only

Reference:baseline +all AMVs

Geo AMV: reference – Modis AMVs

Others:reference +instrument

Preliminary recommendations – OSE, OSSE

“The Global Observing System”, Jean-Noël Thépaut, Data Assimilation Training Course, ECMWF Reading, 25 April- 4 May 2007

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Preliminary recommendations – access to operational systems

Make operational systems more readily available for research

Mutual benefitScientists work on topics of interest to operational agenciesBenefit from operational facilities (models, computer resources, expert help)Operational agencies benefit from latest research resultsIncreases chances to technology transfer from research base to operations

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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

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Preliminary recommendations

Develop observation operatorsFundamental link between data and modelsEssential 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

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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 issuesDevelop 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

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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

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NWP I

Developments driven by operational requirements of forecasting centres

New servicesSeasonal and inter annual forecastsHigh impact weather

New and improved services, based onBetter modelsBetter dataSatellite data are key

Innovation needs close links between R&D and operations

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NWP IIPull through of satellite data for NWP, in Europe

Strong for meteorological data sourcesEg via EUMETSAT SAFsWeaker for non EUMETSAT data

• Ad hoc• But good examples of transfer from research to operational status

eg scatterometer, GOME, altimetryKey satellite requirements

• Low level (1B/C) radiances• Some retrievals (eg Atmospheric Motion Vectors)• Surface gridded fields• Real time delivery (<1 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, OSSEQuantify impact of satellite data on NWP

Comparison of Europe with USAJCSDA

• NASA/NOAA initiative• To accelerate take up of new data sources

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“The Global Observing System”, Jean-Noël Thépaut, Data Assimilation Training Course, ECMWF Reading, 25 April- 4 May 2007

NWP IV

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Instruments have been ranked:Geopotential 500 hPa Southern Hemisphere

SH RMS error 500 hPa geopotential

020406080

100120

Day2 Day5 Day7

(m)

controlamsua

airshirs

scatssmi

amsubcsr

geo amvreference

baseline

AIRS (1 instrument) and AMSU-A (3-4 instruments) constellation clearly emerge from the pack

Control:all

Baseline:conv only

Reference:baseline +all AMVs

Geo AMV: reference – Modis AMVs

Others:reference +instrument

NWP V

“The Global Observing System”, Jean-Noël Thépaut, Data Assimilation Training Course, ECMWF Reading, 25 April- 4 May 2007

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NWP VI

Messages from NWPNWP 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

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Reanalysis I

Long term (eg 40 years) global data sets of past climate using data assimilation

Reliant of latest NWP model + historical dataECMWF leads in Europe

Key forUnderstanding climate trends

Improving both models and data (biases)

ChallengesNeed for improved coupled models

Inhomogeneities in data records

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Reanalysis II

Messages from reanalysisLong 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

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“New” (pre)-operational forecasting I

Ocean forecasting

Chemical weather forecasting

Learning from current practice in NWPReliant on NWP either through loosely or tightly coupled models

GMES Core Services providing a European delivery structure

Far less technically mature than NWPRequirements less precise

Techniques more experimental

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“New” (pre)-operational forecasting II

Data typesOcean 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)

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“New” (pre)-operational forecasting III

Messages Continued development through close research/operational interactionsModels immature in key areas of user interests, eg

• boundary layer chemical forecasts• coupled physical-biogeochemical models and assimilation of

ocean colour dataNeed for better comparison between data and modelsStandards, data formats are still evolving etc

• GMES and INSPIRE are addressing thisTools, 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 GCMsWhat’s 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

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Earth system science II

Global carbon cycleQuantifying surface fluxesQuantifying role played by fireIdentifying weights of key processes in tropics for post-Kyoto negotiations

Atmospheric compositionUnderstanding interactions between climate change and atmospheric composition

CryosphereStrongest 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