Space-Time Variability in Space-Time Variability in Carbon Cycle Data Carbon Cycle Data

AssimilationAssimilation

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!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

A Different StrategyA 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

Modeling & Analysis ToolsModeling & Analysis Tools(alphabet soup)(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)

FCO2 (x, y, t) R(x, y, t) GPP(x, y, t)

Treatment of Variations for Treatment of Variations for InversionInversion

• 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]

FCO2 (x, y, t) R (x, y)R(x, y,t) GPP (x, y)GPP(x, y, t)

SiB SiB

unknown!

unknown!

Ck ,m R,i, j Ri, j ,nCRk ,m,i, j ,n* A,i, j Ai, j ,nCAk ,m,i, j ,n

* i, j ,n t f xy CIN

Flux-convolved influence functions derived from SiB-RAMS

Continental NEE and [COContinental NEE and [CO22]]

• 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.

Seasonal and Synoptic Seasonal and Synoptic VariationsVariations

• 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

Daily min [CO2], 2004

Lateral Boundary ForcingLateral 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)

Average NEESiB-RAMS Simulated Net Ecosystem Exchange (NEE)SiB-RAMS Simulated Net Ecosystem Exchange (NEE)

Filtered: diurnal cycle removed

Filtered: diurnal cycle removed

Ring of Towers: May-Aug 2004Ring 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

Back-trajectory “Influence Back-trajectory “Influence Functions”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

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[CO2(t)](x,y)

Wow!

no info overGreat Lakes

Next Step: Predict 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

Coupled Modeling and Assimilation SystemCoupled Modeling and Assimilation SystemCSU RAMS

Radiation

Clouds

CO2 Transport and Mixing Ratio

Winds

Surface layerPrecipitation

PBL

(T, q)

Biogeochemistry

Microbial pools

Litter pools

Slow soil C

RootsWoodLeaves

passive soil C

allocation autotrophic resp

heterotrophic resp

SiB3

Snow (0-5 layers)

Photosynthesis

Soil T & moisture (10 layers)

Canopy air spaceSfc TLeaf T

H LE NEE

CO2

CO2

• 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

Summary/RecommendationsSummary/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