1 / 44

Regional Flux Estimation using the Ring of Towers

Regional Flux Estimation using the Ring of Towers. Scott Denning, Ken Davis, Scott Richardson, Marek Uliasz, Dusanka Zupanski, Kathy Corbin, Andrew Schuh, Nick Parazoo, Ian Baker, Tasha Miles, and Peter Rayner. Regional Fluxes are Hard!.

page
Télécharger la présentation

Regional Flux Estimation using the Ring of Towers

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. Regional Flux Estimation using the Ring of Towers Scott Denning, Ken Davis, Scott Richardson, Marek Uliasz, Dusanka Zupanski, Kathy Corbin, Andrew Schuh, Nick Parazoo, Ian Baker, Tasha Miles, and Peter Rayner

  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 • 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 seen by RS

  3. Temporal Variations in NEE • Flux is nothing like a constant value to be estimated! • “Coherent” diurnal cycles?, but … • Day-to-day variability of ~ factor of 2 due to passing weather disturbances NEE @ WLEF

  4. Pesky Variability in the Real World High-Frequency Variations in Space … • Managed forests, variable soils, suburban landscapes, urban parks • Disturbance and succession: fires, harvest, etc • Crops: Wheat vs Corn vs Soybeans • Irrigation, fertilization, tillage practice • Wisconsin (ChEAS) flux towers Attempt to “upscale” annual NEE over 40 km: • WLEF = a1 * WC + a2 * LC, • but only if a2 < 0 • decorrelation length scale is very smallon annual NEE!

  5. What Causes Long-Term Model Bias? • Parameters (maybe, but more likely to control variability than bias) • State! • Respiration: soil carbon, coarse woody debris • GPP: stand age, nutrient availability, management • Missing equations! • Physiology is easier to model than site history and management

  6. Our 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” model biases for slow BGC • GOAL: Time-varying maps of sources/sinks consistent with observed vegetation, fluxes, and CO2 as well as process knowledge

  7. Observational Constraints • Satellite imagery & veg maps • spatial and seasonal variations • Flux towers • Ecosystem physiology for different veg types • GPP, Resp, stomates, drought response • Atmospheric CO2 • Average source/sink over large upstream area

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

  9. wpl sobs frs amt lef ring hrv sgp wkt 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 • What causes these huge coherent changes?

  10. 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

  11. Modeling & Analysis Tools(alphabet soup) • Ecosystem model (Simple Biosphere, SiB) • Weather and atmospheric transport (Regional Atmospheric Modeling System, RAMS) • Large-scale 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)

  12. Frontal Composites of Weather Oklahoma Wisconsin Alberta • The time at which magnitude of gradient of density () changes the most rapidly defines the trough (minimum GG , cold front) and ridge (maximum GG) Frontal Locator Function

  13. Frontal CO2 “Climatology” • Multiple cold fronts averaged together (diurnal & seasonal cycle removed) • Some sites show frontal drop in CO2, some show frontal rise … controls? • Simulated shape and phase similar to observations • What causes these?

  14. Deformational Flow • Anomalies organize along cold front • dC/dx ~ 15ppm/3-5° gradient strength • shear • deformation • tracer field • rotated by • shear vorticity • stretching • deformation • tracer field • deformed • by stretching

  15. Ring of Towers • inexpensive instruments deployed on six 75-m towers in 2004 • ~200 km radius • 1-minute data May-August

  16. Ring of Towers Datamid-day only June 9- July 5, 2004 5 ppm over 200 km u ~ 10 m/s z ~ 1500 m ~ 13 mol m-2 s-1

  17. Coupled Model: SiB-RAMS-LPDM • SiB3 – Simple Biosphere Model [Sellers et al., 1996] • Calculates the transfer of energy, water, and carbon between the atmosphere and the vegetated surface of the earth • Photosynthesis model of Farquhar et al. [1980] and stomatal model of Collatz et al [1991, 1992] • Ecosystem respiration depends on soil temperature, water, FPAR, with pool size chosen to enforce annual carbon balance • Parameters specified from MODIS Vegetation imagery (1 km) • RAMS5 – Regional Atmospheric Modeling System • Comprehensive mesoscale meteorological modeling system (Cotton et al., 2002), with telescoping, nested grid scheme • Bulk cloud microphysics parameterization • Meteorological fields initialized and lateral boundaries nudged using the NCEP mesoscale Eta analysis (x = 40 km) • Deep cumulus after Grell (1995); Shallow cloud transports after Freitas (2001) • Lateral CO2 boundary condition from global SiB-PCTM analysis • LPDM - Lagrangian Particle Dispersion Model • Backward-in time particle trajectories from receptors • Driven from 15-minute RAMS output

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

  19. Back-trajectory Analysis • 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

  20. SiB SiB     unknown! unknown! Flux-convolved influence functions derived from SiB-RAMS Treatment of Variations for Inversion • Fine-scale variations (hourly, 20-km pixels) from weather forcing and satellite vegetation data 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]

  21. Maximum Likelihood Ensemble Filter (MLEF) • Closely related to Ensemble Kalman Filter • No adjoint, forward modeling of ensemble of perturbed states or parameters • Propagate estimates of GPP(x,y) and Resp(x,y) along with (sample of) full covariance matrix • Model “learns” about parameters, state variables, and covariance structure over each data assimilation cycle • Explain on whiteboard?

  22. Pseudodata Ring Inversions • 6 short towers plus 396 m at WLEF • 2-hour averaged data (from 1 min) • SiB-RAMS nest at x=10 km • LPDM on RAMS output, convolve with GPP and Resp, influence functions integrated for 10 days • Add Gaussian noise to initial ’s and obs • Estimate GPP and Resp for 30x30 grid boxes centered at WLEF at x=20 km • Nunk = 30 x 30 x 2 = 1800

  23. _ _  = 0.5  = 1.1 Synthetic “Ring” Experiment: MLEF • Solvefor (x,y) on a 20-km grid • “Truth” divided in half (E vs W) • Noise added at different scales (8x N vs 4x S) • Prior:  = 0.75 • Prior smoothing = 6x … solve 6 towers, obs every 2 hours

  24. no info over Great Lakes Wow!

  25.    MLEF Result after 70 Days • Easily finds E-W diffs • Some skill locating anomalies • Better N-S diff in covariance than prior • No flux over lakes, so no skill there!

  26. NACP “Mid-Continent Intensive” (2007)

  27. 31 Towers in 2007

  28. 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 would progressively “learn” about both fluxes and covariance structure

  29. Coupled Modeling and Assimilation System 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 • Add C allocation and biogeochemistry to SiB-RAMS • Parameterize using eddy covariance and satellite data • Optimize model state variables, not parameters or unpredictable biases • Propagate flux covariance using BGC instead of a persistence forecast

More Related