Integration of biosphere and atmosphere observations

Yingping Wang1, Gabriel Abramowitz1, Rachel Law1,

Bernard Pak1, Cathy Trudinger1, Ian Enting2

1CSIRO Marine and Atmospheric Research2 University of Melbourne

Objective

• Land surface model (LSM) as a key component in models for climate or weather predictions;

• In LSM, we represent land biosphere by biome types, and assume that vegetation in each biome type has a set of parameters. Values of most parameters are commonly provided by a lookup table.

• The objective: obtain the best estimate of those parameters in the lookup table using multiple types of data, eg atmospheric concentration and eddy fluxes.

The Carbon Cycle Data Assimilation Scheme (Rayner et al. 2005)

• A biosphere model calculates C flux for a given set of parameters at 2o by 2o;

• Transport model maps the flux to concentration;• Adjoints of both model are available and used in

the optimization;• Cost is calculated as the squared mismatch in

concentration• 57 parameters are optimized with 500

concentration obs per year for 20 years• Estimates of all 57 parameters were estimated

using least square

The Carbon Cycle Data Assimilation Scheme (Rayner et al. 2005)

Key findings:

•Only the ratio of NEP/NPP is well constrained

•Model errors important. Vcmax ranges from 160 mol m-2 s-1 for deciduous shrub to 8 mol m-2 s-1 for C4 grassland!!

Some errors can not be accounted for by parameter tuning

Use the improved CBM (CABLE)

Eight parameters varied within their reasonable ranges

Grey region shows PDF of ensemble predictions

From Abramowitz et al. 2008

Model and model errors

error obsprojectionobs

error randomerror model

state

matrix transition

P

S

wuP

SΦ

P

S

t

t

tt

tt

t

t

t

vHZ

1

1

eparm: parameter error, model calibration

erep: representation error, increasing model resolution

esys: systematic error (statistical model)

)(teeeu sysrepparmt

How big are those errors?

Abramowitz et al. 2006Averaging window size (day)

Parameter error

Systematic error

Random error

But we need estimates of all parameters for global vegetations

• Flux tower and most ecological measurements (except remote sensing) has small spatial coverage;

• Parameter values at similar spatial scales of global climate models should be estimated from fluxes at that scale;

• Atmospheric inversion can provide flux estimates at that spatial scales and a good diagnosing tool.

TRANSCOM III Results (Gurney et al. 2004)

• Obs: monthly mean [CO2] at 75 sites; and uncorrelated;

• Prior uncertainties based on CASA fluxes and uncorrelated

• 94 land regions in CSIRO transport model, and were aggregated to 11 regions;

• 11 transport models, > 2o by 2o.

TRANSCOM III Results (Gurney et al. 2004)

Month Month

Map of covariance/CCAM grid

1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0

1 0

2 0

3 0

4 0

5 0

6 0

7 0

8 0

9 0

- 0 . 3

- 0 . 2

- 0 . 1

0 . 0

0 . 1

0 . 2

0 . 3

0 . 4

0 . 5

0 . 6

Correlation matrix

1-16: Australia

20-30 south America

57-62: Europe

85-95: North America

Combining top down and bottom up

-150 -100 -50 0 50 100 150

-50

0

50

Top down

Coarse resolution

Globally consistent

Results sensitive to priors

Concentration to flux

Bottom up

Fine resolution,

Potentially large error

Results sensitive to parameters

Parameter to flux

Rayner et al. submitted

Wang et al. unpublished

To estimate key parameters in biosphere model

1. Use eddy flux, remote sensing and other ecological measurements to calibrate a process model, and use a statistical model to account for systematic and random errors;

2. Use a biophysical model to provide prior estimates of fluxes and covariance;

3. Use the atmospheric data and other data to retrieve land surface fluxes;

4. Concentration-> global flux -> global parameters and use other estimates at regional scale if possible.

Recent developments

• CO2 satellites will be launched in 2009;

• New technique is being develop to estimate surface fluxes at finer resolution (ca 4o by 8o monthly);

• But the estimates are sensitive to background covariance of fluxes, data error covariance and other assumptions;

• If only fluxes are estimated, we always have more unknown than number of measurements, and lack of predictive capability

• We need estimates of parameters

Using atmospheric data as a diagnosis tool: Southern Hemisphere

South Pole

Blue: obs, green: model, red: CASA

Contribution of source from each semi-hemisphere

Data: GLOBALVIEW-CO2 (2003)

From: Law et al 2006

Southern tropical fluxes

Saleska et al., Science, 302, 1554-1557, 2003

Tapajos, Brazil

0-30oS

Tapajos, Brazil

Seasonality in model opposite to observed. Model seasonality dominated by photosynthesis, observed by respiration

From: Law et al 2006

Model results

Use atmospheric data as a diagnosis tool: Northern Hemisphere site

Blue: obs

Green: CABLE

Red: CASA

Data: GLOBALVIEW-CO2 (2003)

Barrow Ulaan Uul

Mauna Loa Cape Rama

Figure 1. Comparison of the modelled monthly mean concentration by CCAM with CABLE (green) or CCAM using the carbon fluxes as calculated using CASA model (red) with the observed (blue) at four land stations at different latitudes. The latitudes are: 71.32 oN for Barrow; 44.45 oN for Ulaan Uul, 19.5 oN for Mauna Loa and 15.08 oS for Cape Rama.

Integration: top down and bottom up

Concentration and isotopes

Atmospheric inversion

LSM (parameter)

+ stat modelEco data

Prior flux and variance Global flux LSM +stat model

Global parameters

Other regional estimates