Observing and Modeling Requirements for the BARCA Project Scott Denning 1, Marek Uliasz 1, Saulo Freitas 2, Marcos Longo 2, Ian Baker 1, Maria Assunçao Silva Dias 2, Pedro Silva Dias 2 1 Department of Atmospheric Science, Colorado State University 2 CPTEC and IAG Universidad de São Paulo

Balanço Atmosférico Regional de Carbono na Amazônia (BARCA) Characterize horizontal and vertical distributions of atmospheric CO 2 over Amazônia Establish relationships between vertical concentration gradients and exchange fluxes observed at eddy flux towers Directly quantify regional to Basin-scale fluxes in Amazônia using airborne measurements of CO 2 and other tracers in and above the planetary boundary layer (PBL); Test hypotheses that Amazônia is a major net source or sink for CO 2

Proposed BARCA Airborne Missions

Potential Continuous CO 2 Sites Hourly [CO 2 ], [CO]? Augmentation to existing instruments needed at some sites (especially routine, frequent calibration) Manaus ZF2 Aguas Emendadas Rebio Jaru Sinop Natal Caxiuanã Tapajos (km67) Fazenda Sr. David Inflow air, east-west transects, north-south differences

BRAMS Experimental Setup Horizontal grid  x = 50 km 32 levels  z=120 m at sfc, stretching to 1000 m at domain top (25 km) Time step  t = 40 s Nudged lateral boundary conditions along 3 outer grid columns to 6-hourly CPTEC analysis (u, v, w, T, q,  ) Grell cumulus parameterization 25 July through 31 August 2001 (Santarem Mesoscale Campaign, see Lu poster)

Lagrangian Particle Dispersion Modeling Towers: –continuous release at 50 m, particles per hour from each tower Aircraft: –5 vertical profiles every 300km (1hour) from W to E –10 samples at 0.2, 0.5, 1, 1.5, 2, 3, 4, 5, 7, and 10 km) –9000 particles released from each sample, so from each vertical profile LPD model was run backward in time using BRAMS output from Jul 25-Aug 31, 2001 Particles were released from towers during August but traced backward in time a week longer

Influence functions  x  y ppm/(  mol/m 2 s) 8 towers (50m) operating continuously during August 5-hour flight including 5 vertical profiles (0.2 to 10 km) during afternoon of 3 Aug E-006 1E What Surface Fluxes Do Atmospheric Samples See?

Regional Scale Forward simulation of effects of prescribed flux within 500 km of Santarém towers

Ecosystem Respiration GPP Regional Scale Forward simulation of effects of prescribed flux within 500 km of Santarém towers

COBRA 2000 Influence Functions Nearly all the information about surface fluxes in COBRA campaigns was collected in (rare) “missed-approach” sampling within PBL Model parameters determined by optimization of these data Model can then be integrated to produce spatialized fluxes (Gerbig et al, 2003)

Global Context Sparse data constrain only large regions Try to be “smart” about sub-regional distributions Monthly regional patterns to be based on drought stress physiology, edaphic conditions, land mgmt, etc: remote sensing, eddy covariance, plot studies! (e.g., TransCom, Gurney et al 2003) TransCom 3 Sites & Basis Regions Twelve global transport models Monthly estimation of regional flux and uncertainty

12 potential [CO 2 ] observing sites in and near Amazônia were added to each inversion Es timation Uncertainty in Amazon Carbon Budget TransCom 3 setup (76 stations) 0.73 GtC/yr Balanço Atmosférico Regional de Carbono na Amazônia + 12 additional Amazon sites: 0.17 GtC/yr

Temporal Decomposition of Fluxes Impose time-mean balance (“~”) on R and A Determine parameters  R and  A from flux towers, remote sensing, etc Time-mean flux is due to processes not represented in forward model ecosystem respiration (balanced) GPP (balanced) time-mean residual flux

Conclusions Atmospheric [CO 2 ] measurements during BARCA can potentially provide good constraint on regional/basin-scale carbon budget of Amazonia Continuous measurements can dramatically increase constraint on basin-wide fluxes Combined tower-based and airborne network, combined with remote sensing and modeling, could allow strong constraint on regional fluxes Quantitative estimates of regional flux will require assumptions about time-space patterns of fluxes Best basin-scale integration will require understanding of space-time patterns constrained by remote sensing, eddy covariance, plot data