Satellite data, ecosystem models and site data: contributions of the IGBP flux network to carbon cycle science David Schimel, Galina Churkina, Eva Falge, Rob Braswell, James Trembath, [ Or, what networks can and cannot do

Net Ecosystem Exchange A very difficult modeling problem Average NPPs are kg ha NEEs are kg ha The NEE signal is typically <20% of NPP (or respiration) The uncertainties of NPP and NEE data are 25-50% of the mean, typically. Biases are common. Model biases in NPP and respiration that are too small to correct using typical measurements can cause significant biases in modeled NEE

The small difference between two large fluxes (NEE = <1 - 20% of NPP) NEE = GPP - Ra - Rh NBP/NPP 5% Russian Forests 10% Russian Wetlands 16% Russian Grass/Shrublands 25% in EU CANIF sites ~1% in natural conditions

Carbon Uptake Period: the number of days on which NEE is negative (flux from atmosphere to ecosystem)

A Global Scaling Exercise (from the Department of Irresponsible Extrapolation) CUP from flux data CUP from NDVI + ENF * DBF grass crop CUP from flux data NEE from flux data Growing season length appears to be a robust predictor of Eddy Flux NEE

Forest NEE extrapolated from CUP Using: forest type map, separate regressions for broad and needle leafed forests and a satellite-based CUP, all aggregated to 0.5 o. D.I.E.

Extrapolated Forest Sector NEE (High bias) North America1.9 Gt/y Eurasia1.6 Gt/y Why? Mean Forest CUPFraction deciduous (days )(%) North America Eurasia21022 CUP and “broadleaf-ness” are spatially correlated D.I.E.

Where do we go from here? The network is dominated by sites with large positive NEE: are we observing a representative sample? If so, what does this mean? Specifically: The flux network is biased towards aggrading stands years old The eddy flux measurements may have a high bias because of unfavorable measurement conditions at night. Larch covers much of Siberia, does it follow either regression???

Observed NEE NDVI Direct and Remote Measurements Growing season length has similar interannual variability

gC/m 2 /day Modeled and Observed NEE gC/m 2 /day Most of the systematic error occurs in the beginning and the end of growing season

Global regression suggests an average ~3 g m 2 CUP day Time-series suggest ~0.6 g m 2 CUP day D.I.E. Space for time problems

“Space for process” problems: Century simulated global respiration vs T o and the T o response function: what networks cannot do No matter how well you sample, the T o partial derivative can’t be estimated from the spatial pattern

Space for time

Implications for network design: Time-series of forcing and response are needed to understand process: spatial patterns cannot substitute. Systematic sampling of ecosystem states within CUP ranges, e.g., management intensity, age, nutrient status is crucial Ground measurements to link satellite and ground-based measurements, e.g., canopy optical properties, sun photometer, navigation aids, airborne time series data, are needed for extrapolation Process-level focus on seasonal transitions, e.g., spring and fall focus on plant and soil measurements, snow cover, are crucial

The planned restructuring of the IGBP must strengthen the role of experimental networks, and increase their interaction with synthesis and modeling efforts! A major criteria for any new structure for the IGBP: will it strengthen the networks? The experimental networks of the IGBP are a unique feature of the program and distinguish it from modeling and synthetic efforts such as the IPCC and Millennium Assessment