1 / 25

Space-Time Variability in Carbon Cycle Data Assimilation

Space-Time Variability in Carbon Cycle Data Assimilation. Scott Denning, Peter Rayner, Dusanka Zupanski, Marek Uliasz, Nick Parazoo, Ravi Lokupitiya, Andrew Schuh, Ian Baker, and Ken Davis. Acknowledgements: Support by US NOAA, NASA, DoE. Regional Fluxes are Hard!.

seamus
Download Presentation

Space-Time Variability in Carbon Cycle Data Assimilation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Space-Time Variability in Carbon Cycle Data Assimilation Scott Denning, Peter Rayner, Dusanka Zupanski, Marek Uliasz, Nick Parazoo, Ravi Lokupitiya, Andrew Schuh, Ian Baker, and Ken Davis Acknowledgements: Support by US NOAA, NASA, DoE

  2. Regional Fluxes are Hard! • Eddy covariance flux footprint is only a few hundred meters upwind • Heterogeneity of fluxes too fine-grained to be captured, even by many flux towers • Temporal variations ~ hours to days • Spatial variations in annual mean ~ 1 km • Some have tried to “paint by numbers,” • measure flux in a few places and then apply everywhere else using remote sensing • Annual source/sink isn’t a result of vegetation type or LAI, but rather a complex mix of management history, soils, nutrients, topography not easily seen by RS

  3. A Different Strategy • Divide carbon balance into “fast” processes that we know how to model, and “slow” processes that we don’t • Use coupled model to simulate fluxes and resulting atmospheric CO2 • Measure real CO2 variations • Figure out where the air has been • Use mismatch between simulated and observed CO2 to “correct” persistent model biases • GOAL: Time-varying maps of sources/sinks consistent with observed vegetation, fluxes, and CO2 as well as process knowledge

  4. Modeling & Analysis Tools(alphabet soup) • Ecosystem model (Simple Biosphere, SiB) • Weather and atmospheric transport (Regional Atmospheric Modeling System, RAMS) • Large-scale continental inflow (Parameterized Chemical Transport Model, PCTM) • Airmass trajectories(Lagrangian Particle Dispersion Model, LPDM) • Optimization procedure to estimate persistent model biases upstream (Maximum Likelihood Ensemble Filter, MLEF)

  5. SiB SiB     unknown! unknown! Flux-convolved influence functions derived from SiB-RAMS Treatment of Variations for Inversion • Fine-scale variations (hourly, pixel-scale) from weather forcing, NDVI as processed by forward model logic (SiB-RAMS) • Multiplicative biases (caused by “slow” BGC that’s not in the model) derived by from observed hourly [CO2]

  6. Continental NEE and [CO2] • Variance in [CO2] is strongly dominated by diurnal and seasonal cycles, but target is source/sink processes on interannual to decadal time scales • Diurnal variations are controlled locally by nocturnal stability (ecosystem resp is secondary!) • Seasonal variations are controlled hemispherically by phenology • Synoptic variations controlled regionally, over scales of 100 - 1000 km. Let’s target these.

  7. Seasonal and Synoptic Variations Daily min [CO2], 2004 • Strong coherent seasonal cycle across stations • SGP shows earlier drawdown (winter wheat), then relaxes to hemispheric signal • Synoptic variance of 10-20 ppm, strongest in summer • Events can be traced across multiple sites • “Ring of Towers” in Wisconsin

  8. Lateral Boundary Forcing • Flask sampling shows N-S gradients of 5-10 ppm in [CO2] over Atlantic and Pacific • Synoptic waves (weather) drive quasi-periodic reversals in meridional (v) wind with ~5 day frequency • Expect synoptic variations of ~ 5 ppm over North America, unrelated to NEE! • Regional inversions must specify correct time-varying lateral boundary conditions • Sensitivity exp: turn off all NEE in Western Hemisphere, analyze CO2(t)

  9. SiB-RAMS Simulated Net Ecosystem Exchange (NEE) Average NEE

  10. Filtered: diurnal cycle removed

  11. Filtered: diurnal cycle removed

  12. Ring of Towers: May-Aug 2004 • 1-minute [CO2] from six 75-m telecom towers, ~200 km radius • Simulate in SiB-RAMS • Adjust (x,y) to optimize mid-day CO2 variations

  13. Back-trajectory “Influence Functions” • Release imaginary “particles” every hour from each tower “receptor” • Trace them backward in time, upstream, using flow fields saved from RAMS • Count up where particles have been that reached receptor at each obs time • Shows quantitatively how much each upstream grid cell contributed to observed CO2 • Partial derivative of CO2 at each tower and time with respect to fluxes at each grid cell and time

  14. no info over Great Lakes Wow!

  15. Next Step: Predict  • If we had a deterministic equation that predict the next  from the current we could improve our estimates over time • Fold  into model state, not parameters • Spatial covariance would be based on “model physics” rather than an assumed exponential decorrelation length • Assimilation will progressively “learn” about both fluxes and covariance structure

  16. CSU RAMS (T, q) Winds Clouds CO2 Transport and Mixing Ratio PBL Precipitation Radiation Surface layer H LE NEE SiB3 Canopy air space Leaf T Sfc T CO2 Photosynthesis CO2 Snow (0-5 layers) Soil T & moisture (10 layers) autotrophic resp allocation Biogeochemistry Leaves Wood Roots Litter pools heterotrophic resp Microbial pools Slow soil C passive soil C Coupled Modeling and Assimilation System • Adding C allocation and biogeochemistry to SiB-RAMS • Parameterize using eddy covariance and satellite data • Optimize model state variables (C stocks), not parameters or unpredictable biases • Propagate flux covariance using BGC instead of a persistence forecast

  17. Summary/Recommendations • Space/time variations of NEE are complex and fine-grained, resulting from hard-to-model processes • Variations in [CO2] dominated by “trivial” diurnal & seasonal cycles that contain little information about time-mean regional NEE • Target synoptic variations to focus on regional scales • Model parameters control higher-frequency variability … optimize against eddy flux & RS • Time-mean NEE(x,y) depends on BGC model state (C stocks) rather than parameters … optimize these based on time-integrated model-data mismatch • 70 days of 2-hourly data sufficient to estimate stationary model bias on 20-km grid over 360,000 km2

More Related