Carbon Assimilation and Modelling of the European Land Surfaces


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First CAMELS Meeting held at Hadley Centre, Met Office, Bracknell, UK on 29th January 2003.

Present:  Met Office: Peter Cox, Chris Jones, Venkata Jogireedy
               LSCE: Pierre Friedlingstein, Philippe Peylin, Andrew Friend
               MPI-BGC: Martin Heimann, Wolfgang Knorr, Jens Kattge
               FastOpt: Thomas Kaminski
               ALTERRA: Ronald Hutjes, Gert-Jan Nabuurs, Mart-Jan Schelhaas
               UNITUS: Dario Papale
               EFI: Ari Pussinen, Sergey Zudin
               CEH: Richard Harding, Chris Huntingford
               JRC: Michel Verstraete,Nadine Gobron
               EC: Claus Bruning

Apologies: Riccardo Valentini (UNITUS), Philippe Ciais (LSCE)

Date of next meeting: Lisbon, 19th March.

Peter Cox, Introduction/Overview.
CAMELS is a project, part of the CARBOEUROPE Cluster, motivated by Article 3.4 of Kyoto Protocol concerning contribution of managed ecosystems to a countrys carbon balance. In particular, CAMELS will try to deal with the problem of how to distinguish anthropogenic influence on terrestrial carbon budgets (i.e. examine mechanisms behind sources/sinks due to e.g. CO2 fertilisation, Nitrogen deposition, climate change etc.)

CAMELS will make use of data (both forest inventory and flux data) and modelling. It will combine the advantages of inversion modelling (large scale, data constrained) and mechanistic forward modelling (ecophysiological understanding).

CAMELS products:
1.    best estimates and uncertainties of contemporary and historical carbon sinks in Europe and elsewhere
2.    prototype C-cycle data assimilation system (CCDAS)  exploiting data and models to produce estimates of Kyoto sinks.

Work Package 1. Ronald Hutjes (Alterra). Data Consolidation.

i) validation data, e.g. FLUXNET tower sites  some sites have 8 years of data, many just 1 or 2. European tower coverage mainly at forest sites. Carbodata project has already processed some of this data.Simple model such as:  
NEE = (aR + becT)(1-e-dP) calibrated against data.

ii) N deposition  EMEP data over Europe, 1985 onwards. Some data prior to this, but before 1980 mainly use reconstruction. Landuse database  Hyde database has maps for 1750, 1800, 1850, 1900, 1950, 1970, 1990. Natural vegetation  combination of Ramankutty data and BIOME model output.

iii) forest inventory data used with soil data, emissions and land use data to give above/below ground carbon stocks. Changes in stocks give annual fluxes. Some of the maps, especially soil maps, have marked national boundaries  likely due to measurement/recording practices. Role of this in CAMELS is a validation. Key first deliverable for WP1 is flux data to validate terrestrial models for WP2.

Work Package 2. Wolfgang Knorr (MPI-BGC). Model Validation & Uncertainty.

Aims to improve TEMs and come up with optimal parameters and estimates of their uncertainty. The plan is
i)  to identify key uncertainties from literature
ii)  to determine optimal params from flux obs (at individual site level)
iii) to improve process description
iv) to repeat (ii)
v) to validate against inventory data (at regional scale)
vi) to final determination of parameters and uncertainties for use in WPs 3&4

Measurement sites (for flux data) mainly in temperate locations, and mainly forest. Some in Boreal and tropical areas, again mainly forest.

Discussion:  issues of data availability were discussed  not all sites have long timescale data series. Suggested that short timeseries could still be useful - even just diurnal data may be enough to constrain the models for WP2. WP3 then does the long timescale processes.

-    from WP1  met data to drive model
-    soil/veg/water data
-    satellite (FAPAR)
-    fluxes & inventory data
-    to WP 3&4  improved processes
-    optimal model params (by PFT)

Work Package 3. Pierre Friedlingstein (LSCE). Modelling C-20 land carbon balance.

-    Especially over Europe, but global too.
-    Spin up TEMs to 1900 (as in C4MIP)
-    Simulate 1900-2000 land C balance with high/low param values from WP2. (may use CCMLP protocol, but Martin H question appropriateness of this).
-    Use results to refine parameter values from WP2
-    Try to separate impact of mechanisms  e.g. land use change, nitrogen deposition, CO2 fertilisation, climate change.

-    WP1 to provide forcing data, land-use, N, CO2
-    WP1 to provide calibration & validation data (fluxes & inventories)
-    WP2 to provide high/low estimates of parameters
-    WP3 provides long-term constraints on parameters to compliment WP2s short term constraint
-    WP3 to provide initial conditions for WP4 (i.e. state of contemporary biosphere).

Work Package 4. Peter Cox (Hadley). Development of DA system.

Combine data & models to estimate sources/sinks @ resolution required by Kyoto.

-    need inverse of TEMs and atmos transport models.
-    Use within CCDAS to adjust parameters in models (use cycle of 25 years of flux data). offline approach.
-    online approach  more like existing NWP data assimilation schemes  nudge model Carbon values in real time to derive sources/sinks

The inputs for this work package are
-    data from WP1
-    TEMs from WP2
-    Initial conditions from WP3

Discussion on whether or not site data-derived parameters scale up to grid scale (i..e. do we still get good agreement with original flux data at site level if we put final parameters back into WP2?)

Work Package 5. Dario Papale (UNITUS). Information dissemination.

-    construct website (for use by both scientists and policy makers)
-    produce policy-maker summaries of annual reports  suitable for use at, e.g. CoP etc.

Contributions to other WPs:
-    gap filling of data for WP1
-    neural net to process site data to regional scale.

Relevant websites:
-    Carbodata:
-    Tacos:

Afternoon session  Partner Plans

1. European Forest Institute (EFI). Ari Pussinen.

Forest inventory based carbon budgeting. Combine inventory data with satellite data.
Get from inventory (volume) data to carbon balance by model using wood density, % C content, age class info

Discussion  do we need age class information in the models? It was agreed that this could become a significant factor in models (especially if they are to cope with forest management), and hence this data would become an even more valuable validation source.

2. CEH. Richard Harding.

Mostly involved in WP2, will be using MOSES.

See this work as different from previous studies that try to find optimal parameters at a single site. Here we want parameters to be consistent across many sites. Will present a challenge as this has not been done before.

Possible problem with energy budget data from tower sites? They generally cant close their budget, and it is suspected (by RH) that the problem is in the latent heat component. Therefore we should exercise caution when deciding what data to be used.

There was then some discussion at this stage about whether our models tend to be over-parameterised, and if so whether we need to confine the procedure to a subset of parameters (i.e. choose some to be held fixed).

3. JRC. Nadine Gobron.

Measure FAPAR from remote sensing.

Aim to produce useful, quantitative, reliable and accurate data from remote sensing for use in WP4.
2 km data from 1997 to 2001.
Method gives a lower rms error and higher signal to noise ratio (by factor 3) than antiquated method.
Daily data has cloud gaps, and only partial satellite coverage, so plan to produce 10-day composite. Monthly could also be done.
Aims to be able to use different sensors (e.g. SeaWiFS, MISR, MERIS) with results being transparent. This is not the case for NDVI data, which is sensor-dependent.

Discussion on data format required  decided that 10 day data would be better. Still to be agreed  whether JRC to produce high resolution data for modellers to process to their own grid, or JRC to produce it already at coarse resolution. Some preference for the former because different models have different grids.

Nadine finished with some questions.

1.    Which associated products, i.e. Rectified channels and BRF TOA, may be required by WP4?
2.    What temporal resolution (10 days or monthly) is useful for European maps?
3.    What spatial resolution is required for specific sites (both in and outside Europe)?
4.    How should the products be provided, e.g. ftp site, support media and formats, contact point, etc.?

4. FastOpt. Thomas Kaminski.

Calibration step of CCDAS  via cost function and variational approach to find optimal parameters. Iterative minimisation of cost function by gradient descent method.

While dJ/dx gives gradient information for descent algorithm, can also get a measure of uncertainty from d2J/dx2 (Hessian matrix) which measures curvature at xopt.

The technique requires derivative code i.e. code to get the derivative of the TEM. This can be constructed automatically. FastOpts role is to support this process.

End of afternoon - Discussion.

i)    Wolfgang suggested that some of the role of inventory data be moved from WP2 to WP3. Pierre and Gert-Jan agreed in principle (for large scale inventory data  WP2 just requires site data). Claus warned against structural changes to the WPs. ACTION: WK, PF, G-JN to agree on distribution of inventory work between WP2 and WP3

ii)    Wolfgang noted need for error estimates on flux data. Richard said that this is included under Consolidation so WP1 will be producing this. Jens Kattge has produced a questionnaire about requirements such as this.

iii)    There is a need to select some sites. It was suggested that one for each PFT be chosen.

iv)    CAMELS website  can be set up in Jena, and be run from Bracknell. Suggested that it should be part free access and part password protected.

Meeting closed at 4.30pm.