1. Data Assimilation for the Carbon Cycle funded by NASA’s Modeling and Analysis Program
PI: Steven Pawson (GMAO)
Atmosphere:
Transport: Julio Bacmeister (GMAO), Randy Kawa (GSFC)
Assimilation and Inversion: Ivanka Stajner, Andy Tangborn (GMAO), Scott Denning (CSU)
Radiance modeling and retrieval: Denis O’Brien (CSU)
Land:
Surface temperature assimilation: Rolf Reichle (GMAO)
Land carbon: Jim Collatz (GSFC), Scott Denning (CSU)
Fossil fuel inventory: David Erickson (ORNL)
Ocean:
Ocean carbon: Scott Doney (WHOI); Watson Gregg (GMAO)

2. Objectives
Expand the capabilities of NASA/GMAO’s GEOS-5 modeling and analysis system for carbon-cycle science
Develop and implement an infrastructure for “model-data fusion” for carbon cycle studies, optimizing top-down and bottom-up constraints on sources and sinks
Exploit NASA’s datasets and modeling capabilities to enhance our understanding of the carbon-cycle 

3. Schematic of the Carbon Data Assimilation System

4. Research Themes and Plans Built Around GEOS-5 System
Analysis of high-resolution global simulations of atmospheric carbon and sensitivity studies
Separation of effects of “transport error” and “source-sink uncertainty” (model perturbations); isolate impacts on assimilation and inversions (OSSEs; real data)
Two-stage development of “assimilation”:
Stage 1: examine consistency of top-down and bottom-up estimates of CO2 (+ CO, CH4) sources and sinks ( )
Stage 2: develop optimal constraints for a coupled atmosphere-land-ocean assimilation system ( )
Products: CO2 concentrations and flux maps (AIRS, MODIS, …, met. analyses) for 2002 onwards; add OCO after launch

5. Stage 1: Consistency of Top-Down & Bottom-Up Source-Sink Estimates
The first stage of the proposal will follow this sequence:
Determine a first-guess source-sink distribution Sf from data-constrained land and ocean models, along with uncertainty estimates
Run the atmospheric assimilation at high resolution, ingesting AIRS level- 1b radiances and in-situ data into the additional carbon modules; later use OCO data
Use high-density analysis increments in an inversion procedure, to produce coarse-grain (say 500km²) "corrections" to S
Investigate the consistency of the improved S by examining parametric uncertainties in the underlying land- and ocean-carbon models - can we reconcile the flux estimates?
Test concept in an OSSE framework before using real data: OSSEs allow degradations to be included systematically

6. Transport of CO in GEOS-4 Real-time forecasting for ICARTT/Intex-NA Field Mission

7. Uncertainty of Convective Transport Radon profiles using GEOS-4/5 Convection
Dashed line: GEOS-4; Solid line: GEOS-4 with GEOS-5-like convection

8. Schematic of the Challenges Posed by Uncertainty in Sub-Grid Transport
True source
True trajectory, from source to cloud, then detraining
True cloud
Cloud
In analysis
Source correction applied in wrong location
Trajectory deduced from analyses
Obs.
Assimilation generates analysis increment to optimize observation with model
This is a real simplification, showing only incorrect location of a single cloud. We’d really be dealing with multiple clouds, a range of detrainment levels, …
This is one of many scenarios: need to study occurrence of convection and regions of inflow-outflow - parameter sensitivity in model
Field mission data, Cloudsat/CALIPSO, etc., will be used along with trajectories from GEOS-5