space-time variability in carbon cycle data assimilation scott denning, peter rayner, dusanka...
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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
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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
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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
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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)
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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
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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.
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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
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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)
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Average NEESiB-RAMS Simulated Net Ecosystem Exchange (NEE)SiB-RAMS Simulated Net Ecosystem Exchange (NEE)
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Filtered: diurnal cycle removed
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Filtered: diurnal cycle removed
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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
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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)
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Wow!
no info overGreat Lakes
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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
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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
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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