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Quick Launch > FLUXNET Blog > Posts > Notes from FLUXNET Berkeley June 2011 workshop - Topic 2 Break-out
June 08
Notes from FLUXNET Berkeley June 2011 workshop - Topic 2 Break-out

Variance, Uncertainty, & Data Products

Focused on three main topics


Variability arising from ecosystem and human causes

Interannual variability and anomalies

Variability arising from climate forcing


Characterization and quantification of errors


What are we missing


Disturbance not well represented in models

Define disturbance as natural component of ecosystems instead of stochastic events

Networks, and remote sensing do not capture longer time scale processes (e.g., processes with 30-50 year return periods)

Need flux tower chronosequences in much larger set ecosystem types

Character/severity of disturbance matters

Much effort in remote sensing towards developing data sets

Fire, insect outbreaks – lots of disagreement across products

Mismatch between what remote sensing can/is providing and what models need

To most effectively link carbon/water consequences of disturbances fluxnet/modeling community needs to define and communicate needs to remote sensing community


Interannual Variation and Anomalies

Interannual variability is hard to capture

Low variance hard to explain

Opportunity to evaluate capacity of models and remote sensing  to capture dynamics by focusing on big events/anomalies

Low hanging fruit to help understand what we can and cannot detect and model

Interannual variability/Event-based analysis

Hydrologic anomaly in 2011; European heat wave 2003/2010; Amazonian droughts

Need better information/understanding of drivers, particularly lagged or cumulative effects

Remote sensing of phenological anomalies and changes in seasonality

Exploit information at site level from webcams (Andrew, Lisa)


Uncertainty (in models and data)

Characterization of uncertainty is difficult

Multiple sources of error propagate through model results

Structural error, calibration error, forcing error, representation error (sampling uncertainty) – Enting et al.

Errors in met drivers and remote sensing inputs used for large scale models need more attention

Gaps in sampling 

Geography matters (but what can you do about it?)

How can we think about more effective sampling

Biogeographic stratification; clustering of remote sensing/ecoregions, etc

Stratification based on spatio-temporal variation in model outputs

Need community discussion of how to characterize and quantify sources and magnitudes of uncertainty; uncertainty products


Data Products

MODIS subsetting tool at ORNL DAAC provides model to apply other remote sensing data sets at fluxnet sites

One stop shopping is good (DAAC, NEX)

Preliminary wish-list includes

Field measured ecophysiological variables used by models (e.g., Vmax)

Medium spatial resolution (10-50 m) maps of model PFTs at fluxnet sites

Landsat archive (time series in support of for e.g.,  disturbance histories) 

Hi-Res Data (<1m) for upscaling (Quickbird, etc)

LIDAR: DESDynI is on hold, but still opportunies from airborne 

Hyperspectral (AVIRIS,Hyperion)

Need to lower barriers to access

Probably many others

Remotely sensed PAR


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