“Global Carbon Project” pilot case study (CEA-LSCE)

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QUAlity aware VIsualisation for the Global Earth QUAlity aware VIsualisation for the Global Earth Observation system of systemsObservation system of systems

Kick off meeting. February 17th, 2011

“Global Carbon Project”

pilot case study

(CEA-LSCE)

Philippe Peylin, Philippe Ciais,

Pep Canadell, Zegbeu poussi


2

atmospheric CO2

ocean

land

fossil fuel emissions

deforestation

7.6

1.5

4.1

2.22.8

2000-2006

CO2 f

lux

(Pg

C y-1

)Si

nkSo

urce

Time (y)

Perturbation of Global Carbon Budget (1850-2006)

Canadell et al. 2007, PNAS

?


3

Global Carbon Project

?


4

Global Carbon Pilot case

Which products can we provide ?(based on models & data)

What are the associated Quality Data ? (still “poorly” developed)

What are the potential Metadata ?

5

Global carbon product

Direct Model simulations

Meteo forcing &surface description

Land surface& oceanmodels

Estimated surfaceC fluxes

Data – fusion approaches


6

Direct estimates of Carbon fluxes

Several Product

with different data Quality

Global land ecosystem model simulations

Global ocean model simulations


7

Meteorological forcing

Land surfaceDescription (Veg. soil,..)

Land surfacemodel

Estimated surfaceC fluxes

• Several model simulations (TRENDY / RECCAP project)

• For each model possibly several simulations (different forcing)

• Product: - Global annual/monthly C fluxes

- Derived quantities: Trend in land C-sinks

• Quality data : - on the input data (forcing , land cover)

- derived from the multi-model realization

• Metadata : - information on the protocol & models

Land ecosystem model simulations

8

Quality of product

usually arise from:

- evaluation against

other estimates/proxy

- potentially error

propagation

- Assessment of

model strength

- How to use multi

model ?

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Product Evaluation as a Quality measure

Diagnose Trend in Land fluxes

Evaluation against

other products ?

(i.e. EO data)

How to facilitate the link with other products ?

- Climate data- land cover changes/use- forestry data- biomass burning- soil data (moisture)- crop yields

10

Case of Data-Assimilation: atmospheric inversion…

Inverse optimization

key features • Combination of 2 sources of information !• Only ~ 100 stations for many fluxes to solve for

Prior flux information Transport model

Atmospheric data

Optimized fluxes

Several approaches

• Flux resolution• Transport model• Level of prior inform. • Optimization algorithm


11

Global carbon pilot case

Data availability

Surface flux Maps (3D) : - weekly to monthly resolution- common grid : 1x 1 degree- several variables (Net flux, Gross fluxes, …)

Spatially integrated fluxes :- Time series for a set of regions- different temporal filtering

(trend, smooth curve,..)


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Associated Quality Data (mainly uncertainties)

Surface flux Maps (3D) : - uncertainties (in the form of std-dev) (often at lower temporal resolution)- use the spread between the different estimates

Not yet completely defined !

Spatially integrated fluxes :- Uncertainties for each time step - error covariance matrix at low temp. resol.- use the spread btw estimates + individual errors


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Associated Metadata

Input data : Text description of the input data

Model (Data Assimilation system)

flow chart, little text documentation….description of the “uncertainty calculation”

Estimated fluxes & uncertainties :possibly “qualitative description of accuracy”as a function of space & time aggregation.


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Carbon fluxes interannual variations

Europe

N. America N. Atlantic

N. Asia

LSCE_an_v2.1JENA_s96_v3.2CTracker_EU

LSCE_var_v1.C13_MATCHCTracker_US

TRCOM_meRIGC_patra JMA_2010

C13_CCAMNCAM_Niwa

?

?

?

How to associate a Quality Measure ?

15

Quality indications from model spread ?

Mean flux estimate RMSE estimate

Estimates are not independent ? Uncertainty on the uncertainties is very large !

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Summary …. Carbon flux products : increasing number but no common visualization framework

As part of GCP we can provide a large set of fluxes & some associated errors, few metadata...

How to use the tools from GeoViQua ?- Incorporate them in our Portal Developments or provide the datasets to GeoViQua ?- Large volume of netcdf files…

We need tools to derive overall quality measures from different estimates (with different uncertainties) and to display them..

We are seeking for a Post-doc….


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Carbon Cycle Web portals


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GCP: atmospheric data

In situ data- very accurate but sparse - spatial representativity ?

- Instrumental failure ?- Quality Data exist..

(visualisation under progress)

satellite data - accuracy issues (biases) !- accuracy might vary with

space- cloud/aerosols…

contamination


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Carbon Tracker Web site (US)

N. America

?


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GEO carbon agenda…..

• Record changes in atmospheric CO2

• Estimate fossil and land use derived emissions

• Understand land and ocean carbon sinks

– Measure fluxes/concentrations

– Understand processes

– Model time & space evolution

in order to predict future of the Earth System


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Model – Data Fusion for GCPD

ata

un

cert

ain

ties

Dat

a u

nce

rtai

nti

es

Flux uncertainties difficult to estimate !

Model uncertainties

UncertaintiesModel – Data fusion

(4D var scheme)

(Baye’s theorem)

Variational / Matrix / approaches


23

GCP: Land ecosystem data

In situ data- very accurate but sparse - spatial representativity ?

- “Quality” not well quantified..

satellite data - accuracy vary with space..- saturation signal with veg.

activity- link to vegetation function ?


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GCP: Ocean data

Ship data- accurate but sparse

- spatial representativity ?- Spatial coverage ?

satellite data - accuracy issues (biases) !

- link to ocean biogeochemistry ?

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“Global Carbon Project” pilot case study (CEA-LSCE)

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