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Using AmeriFlux Observations in the NACP Site-level Interim Synthesis

Using AmeriFlux Observations in the NACP Site-level Interim Synthesis. Kevin Schaefer NACP Site Synthesis Team Flux Tower PIs Modeling Teams. Do models match observations? If not, why?. 47 Flux Tower Sites. 30 Models. 36 AmeriFlux 11 Fluxnet Canada. 24 submitted output 10 runs per site.

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Using AmeriFlux Observations in the NACP Site-level Interim Synthesis

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  1. Using AmeriFlux Observations in the NACP Site-level Interim Synthesis Kevin Schaefer NACP Site Synthesis Team Flux Tower PIs Modeling Teams

  2. Do models match observations? If not, why? 47 Flux Tower Sites 30 Models 36 AmeriFlux 11 Fluxnet Canada 24 submitted output 10 runs per site

  3. Analysis Projects Published Submitted

  4. Products Derived from Flux Data • Gap-filled observed weather (Ricciuto et al.) • BADM files (everyone) • Gap-filled fluxes & Uncertainty (Barr et al.) • Random • U* threshold • Gap-filling Algorithm • Partitioning Algorithm

  5. Random Uncertainty (Barr et al.) Random eNEP~4% Re U*theNEP~1.3% Re Needleleaf Forest Broadleaf Forest Mixedwood Forest Juvenile Forest Wetland Grassland Shrubland Cropland ▲ USA ● Canada Annual eNEP (g C m-2 y-1) Annual Re (g C m-2 y-1)

  6. BADM Files • Extremely useful to modelers • Soil texture • Site history • Initial pools sizes • Leaf Area Index • We strongly encourage more submissions

  7. Weather Uncertainty (Ricciuto et al.) Bias in radiation produces bias in GPP

  8. Agriculture Sites (Lokupitiya et al.) Need crop specific parameterizations US-Ne3 Corn Soybean Corn Soybean

  9. Wetland Sites (Desai et al.) Residuals correlate to water table depth Models should include water table dynamics Reco residuals Residuals mmol m-2 s-1 GPP residuals Residuals mmol m-2 s-1 Water Table Depth (cm)

  10. Spectral NEE Error (Dietze et al.) Annual • Error peak at diurnal & annual time scales • Errors at synoptic & monthly time scales Month Diurnal Synoptic Not Significant

  11. NEE Wavelet Coherence (Stoy et al.) Models match observations only some of the time SiB at US-UMB Annual Significant Month Synoptic Time Scale (hours) Diurnal Hour

  12. NEE Seasonal Cycle (Schwalm et al.) Add vegetation pools 0.6 0.5 Taylor Skill 0.4 0 3 4 6 7 8 9 Number Veg Pools Improve prognostic phenology 0.6 Phenology Taylor Skill 0.5 0.4 Semi-Prog Prognostic Prescribed 0.6 Add soil layers Taylor Skill 0.5 0.4 0 1 2 7 9 10 11 15 3 Number Soil Layers

  13. Phenology (Richardson et al.) Early/late uptake means positive GPP bias Models need better phenology

  14. Regional vs. Site (Raczka et al.) Enzyme kinetic models biased high LUE models biased low Flux Towers Light Use Efficiency Enzyme Kinetic

  15. GPP Annual Bias (Schaefer et al.) Slope of LUE Curve drives Annual bias Models need better Vmax, leaf-to-canopy scaling, … US-Me2 Light Use Efficiency Curve Observed Simulated Daily Average GPP (mmol m-2 s-1) Daily Average Shortwave Radiation (W m-2)

  16. Areas For Model Development • Better Phenology • More soil layers • More vegetation pools • Slopes to LUE curve • Water table dynamics • Crop parameterizations

  17. Extra Slides

  18. Annual GPP Bias due to Phenology Evergreen sites Deciduous sites 40±80 160±145 -5±65 75±130

  19. Multi-Model wavelet Coherence Scale (hours)

  20. NEE Seasonal Cycle (Schwalm et al.) Our 1st published paper! Perfect Model Taylor Skill Normalized Mean Absolute Error Chi-squared

  21. U*th vs. Random Uncertainty (Barr et al.) Needleleaf Forest Broadleaf Forest Mixedwood Forest Juvenile Forest Wetland Grassland Shrubland Cropland ▲ USA ● Canada U*th Annual eNEP (g C m-2 y-1) Random Annual eNEP (g C m-2 y-1)

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