Integration of biosphere and atmosphere observations Yingping Wang 1, Gabriel Abramowitz 1, Rachel Law 1, Bernard Pak 1, Cathy Trudinger 1, Ian Enting 2 1 CSIRO Marine and Atmospheric Research 2 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 2 o by 2 o ; 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 mmol 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 e parm : parameter error, model calibration e rep : representation error, increasing model resolution e sys : systematic error (statistical model)

How big are those errors? Abramowitz et al Averaging 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, > 2 o by 2 o.

TRANSCOM III Results (Gurney et al. 2004) Month

Map of covariance/CCAM grid Correlation matrix 1-16: Australia south America 57-62: Europe 85-95: North America

Combining top down and bottom up 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 4 o by 8 o 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, , 2003 Tapajos, Brazil 0-30 o S 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) BarrowUlaan Uul Mauna LoaCape 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: o N for Barrow; o N for Ulaan Uul, 19.5 o N for Mauna Loa and o S for Cape Rama.

Integration: top down and bottom up Concentration and isotopes Atmospheric inversion LSM (parameter) + stat model Eco data Prior flux and varianceGlobal flux LSM +stat model Global parameters Other regional estimates