Desai ChEAS 2005

Multi-tower Synthesis Scaling of Regional Carbon Dioxide Flux: 

Multi-tower Synthesis Scaling of Regional Carbon Dioxide Flux Another fine mess of observed data, remote sensing and ecosystem model parameterization Ankur Desai Penn State University Meteorology Dept. ChEAS Meeting VII June 2005

Goals: 

Goals Identify key processes of within-site and cross-site variability of carbon dioxide flux in space and time with stand-scale observations Develop simple multiple flux tower synthesis aggregation methods to test the hypotheses that stand-scale towers can sufficiently sample landscape for upscaling to regional flux Parameterize and optimize ecosystem models of varying complexity to the region using biometric inventory, remote sensing and component flux data and test effect of input parameter resolution and type on model performance Constrain top-down regional CO2 flux using multi-tower concentration measurements, and simple Eulerian and Lagrangian/stochastic transport schema

ChEAS region and sites: 

ChEAS region and sites 13 stand-scale flux towers, 1 tall tower, new roving towers Legend MODIS IGBP 1km landcover

Interannual variability of NEE: 

Interannual variability of NEE Interannual variability of NEE is coherent at many but not all sites. This does not hold as well for GEP or ER

Intercomparisons and Upscaling: 

Intercomparisons and Upscaling

Flux tower spatial variability: 

Flux tower spatial variability Stand age is a strong driver of variability within specific cover types

CO2 flux variation drivers: 

CO2 flux variation drivers Canopy height serves as a good proxy for stand age Canopy height is well correlated to NEE and GEP, but not to ER, as one might expect The relationship holds for multiple vegetation types, especially for GEP Thus, remotely sensed measurements of forest height, e.g., canopy lidar, could be beneficial to regional scaling

Multi-tower aggregation method: 

Multi-tower aggregation method While mature hardwood sites are dominant in the 40-km radius around WLEF region according to FIA and 30-m Wiscland data, wetlands and young and intermediate aspen sites cannot be ignored Simple method used to aggregate flux tower data using land cover and FIA data and tower derived parameters:

Multi-tower aggregation results: 

Multi-tower aggregation results Multi-tower synthesis aggregation and footprint weighted decomposition results for 40-km radius are in very close agreement Tall tower has greater ER and smaller NEE compared to bottom-up methods

Multi-tower aggregation results: 

Multi-tower aggregation results

Regional flux comparisons: 

Regional flux comparisons Convergence in regional estimates of CO2 flux These estimates are larger than tall tower flux Reasons remain elusive

Ecosystem modeling: 

Ecosystem modeling Competing effects of ecosystem model complexity and data assimilation / parameterization in the upper Midwest Examine two models BIOME-BGC –stand-scale single-layer BGC model ED – gap-scale model with explicit disturbance/mortality/size Assimilate ChEAS area ecosystem information Remotely sensed land cover, phenology FIA stand age distribution, harvest rates, land use Component flux optimized PFT rates and decomposition rates Compare model to tall tower and other regional estimates Compare to: multi-tower aggregation, footprint decomposition, ABL budget based methods Assess impact of model complexity Assess role of data optimization, scale, density Predict future changes in regional CO2 flux

Biome-BGC: 

Biome-BGC Daily time step relatively simple biome/stand-scale ecosystem process model Stand age and disturbance can be externally prescribed Initial work here will be used with more elaborate scaling for currently ongoing roving tower/scaling project by F.A. Heinsch, U. Montana

Ecosystem Demography model: 

Ecosystem Demography model Moorcroft, P. R, G. C. Hurtt, S. W. Pacala, A method for scaling vegetation dynamics: the ecosystem demography model (ED), Ecological Monographs, 71, 557-585, 2001. Explicit consideration of stochastic disturbance events, effect of stand age and mortality

Remote-sensing: 

Remote-sensing IKONOS 4-m 10x10 km around tall tower (courtesy B. Cook) Legend:

Spatial resolution and land cover: 

Spatial resolution and land cover Land cover in region is highly sensitive to resolution due to large number of small area cover types, especially wetlands Land cover change is also important due to logging and disturbance

Incorporation of FIA data: 

Incorporation of FIA data FIA statistics on age, biomass, mortality and CWD can be used to prescribe model parameters

Multi-tower ABL budget: 

Multi-tower ABL budget Simple Eulerian models with 1-D ABL depth model and NOAA aircraft CO2 profile data can be used to test ring of tower validity and provide confidence for inversion More sophisticated stochastic Lagrangian model, similar to COBRA, to be developed to test methods to assimilate multi-tower synthesis data

Conclusions: 

Conclusions Coherent variations in time for NEE across most sites but not as much for ER and GEP Stand age, canopy height, cover type can explain large proportion of cross-site variation Convergence is seen in bottom-up and top-down regional flux estimates – but they generally differ from tall-tower flux, except when “reweighted” for footprint contribution Ecosystem models to be run this summer Resolution of remotely sensed data can have large impact on scaling results in heterogeneous region Simple budget methods with “ring of towers” suggests that more complex inversions will work Multi-tower work here complements single-tower footprint and budget work of W. Wang and tall-tower modeling of D. Ricciuto

Some publications: 

Some publications Cook, B.D., Davis, K.J., Wang, W., Desai, A.R., Berger, B.W., Teclaw, R.M., Martin, J.M., Bolstad, P., Bakwin, P., Yi, C. and Heilman, W., 2004. Carbon exchange and venting anomalies in an upland deciduous forest in northern Wisconsin, USA. Agricultural and Forest Meteorology, 126(3-4): 271-295 (doi:10.1016/j.agrformet.2004.06.008). Desai, A.R., Bolstad, P., Cook, B.D., Davis, K.J. and Carey, E.V., 2005. Comparing net ecosystem exchange of carbon dioxide between an old-growth and mature forest in the upper Midwest, USA. Agricultural and Forest Meteorology, 128(1-2): 33-55 (doi: 10.1016/j.agrformet.2004.09.005). Desai, A.R., Noormets, A., Bolstad, P.V., Chen, J., Cook, B.D., Davis, K.J., Euskirchen, E.S., Gough, C.M., Martin, J.M., Ricciuto, D.M., Schmid, H.P., Tang, J. and Wang, W., submitted. Influence of vegetation and climate on carbon dioxide fluxes across the Upper Midwest, USA: Implications for regional scaling, Agricultural and Forest Meteorology. Heinsch, F.A., Zhao, M., Running, S.W., Kimball, J.S., Nemani, R.R., Davis, K.J., Bolstad, P.V., Cook, B.D., Desai, A.R., et al., in press. Evaluation of remote sensing based terrestrial producitivity from MODIS using regional tower eddy flux network observations, IEEE Transactions on Geosciences and Remote Sensing.

Ph.D. plans: 

Ph.D. plans May: ChEAS meeting, fieldwork Jun-Aug: Ecosystem model parameterization and runs, potential return visits to Montana/Harvard for model work July-Aug: Top-down Lagrangian ABL budget Jun-Oct: ChEAS special issue paper reviews Sep: pre-dissertation defense committee meeting Sep-Dec: dissertation writing, redo footprint model, add 2004 tower data to 1st chapter, finalize multi-tower aggregation chapter, apply to jobs Sep: present at International CO2 conference, Boulder, CO Oct: ChEAS fall fieldwork Oct-Nov: present at Ameriflux, Boulder, CO Dec: present at AGU, San Francisco, CA Dec-Feb: finish dissertation, send to committee and to format review Jan: present ABL research at AMS, Atlanta, GA? Mar: defend dissertation! Mar-Sep: submit final model results for publication, party, travel Fall 2006: post-doc?

Thank You: 

Thank You ChEAS