# Presentation: 169097

## Data Assimilation for the Carbon Cycle

- Steven Pawson (PI)
- Global Modeling and Assimilation Office
- NASA Goddard Space Flight Center

- NASA HQ Carbon and Ecosystems Site Visit
- March 29, 2006

## Objectives of the Project  

- Expand and enhance the capabilities of GEOS-5 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 _x0003_

## Schematic of the Planned Carbon Data Assimilation System  

## Science Team   

- NASA Goddard Space Flight Center:
- GMAO: Pawson (PI), Bacmeister, Gregg, Reichle, Stajner, Tangborn
- Atmospheric chemistry and dynamics: Kawa
- Biospheric Processes: Collatz
- Colorado State University:
- Land modeling and atmospheric inversions: Denning
- Radiance modeling and retrievals: O’Brien
- Oakridge:
- CO2 Modeling and computing: Erickson
- Woods Hole:
- Ocean carbon: Doney

##  Research Themes and Plans 

- Analysis of high-resolution simulations of atmospheric carbon and sensitivity studies
- Separation of problems arising from “transport error” and from “source-sink uncertainty” and their impacts on assimilation and inversions
- Two-stages of proposal:
- Stage 1: examine consistency of top-down and bottom-up estimates of CO2 (+ CO, CH4) sources and sinks (2005-2008)
- Stage 2: develop optimal constraints for a coupled atmosphere-land-ocean assimilation system (2008-2010)
- Products: CO2 concentrations and flux maps (AIRS, MODIS, ..., met. analyses) for 2002 onwards; add OCO after launch

##  Tropical Storms in GEOS-5 

*Courtesy Xin Lin & Julio Bacmeister*

- Hurricane Katrina (Aug 2005)
- Accumulated rainfall (mm)
- Global, quarter-degree forecasts

## GEOS-5 GCM STRUCTURE

- AGCM

- CAP

- HISTORY

- RADIATION

- COLUMN PHYSICS

- SOLAR

- IR

- MOIST

- TURB

- SURFACE

**CHEM**

- LAKE

**OCEAN SKIN**

- DYNAMICS

- GWD

- FVCORE

- (Ts,Fi...)

**VEGDYN**

**CATCH**

- ICE

- LAND

- (Ts,Fi...)

**SURFACE**

- DYNAMICS

- Data AGCM

- COUPLED

- OGCM

- Data OGCM

- Poseidon

**OBIO**

**CICE**

- MOM4

- MITogcm

- Poseidon

## 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
- Additionally, we will perform a suite of high-resolution model experiments to characterize error that arises from transport uncertainty and how this may propagate through the assimilation and subsequent inversion steps

## Determination of First-Guess Fluxes from Land & Ocean Models  

- Ocean estimates: physical ocean plus ocean biogeochemistry (WHOI, GMAO)
- Land estimates: physical land model plus biophysical and disturbance (fire) modules (GSFC, CSU)
- Ensure models are run with physical constraints from GEOS-5 to ensure consistency
- Inventories of fluxes, e.g., fossil fuels: (ORNL, CSU)
- Use CO2, CO, CH4, if possible

## Schematic of Land Carbon Model   

- Interface to atmosphere

- Input of remote-sensing products

## Constituent-Related Studies with GEOS-4

- Off-line atmospheric carbon transport using analyzed winds (Kawa et al., 2004)
- Meteorological constraints for carbon in land (Collatz, Kawa et al.) and ocean (Gregg) models
- On-line CO, ozone and other species for studies of long-range transport etc.
- Constituent forecasting in support of NASA’s field missions:
- Intex-NA, June-July 2004: tagged CO, CO2, O3, etc.
- Polar Ave, Jan-Feb 2005: stratospheric chemistry
- Houston Ave, Costa-Rican Ave: aerosols and CO
- Intex-B, March-May 2006: CO, O3, etc.

## Demonstration of Transport Capabilities (CO in GEOS-4)  

## Overview of Sequential Assimilation (3D Variational Scheme in GEOS-5)   

- 	Where:
- H is the “observation operator” (spatial interpolation of xb; converting, say,  temperature to radiance using the forward model, ...)
- B is the background (forecast) error covariance
- R is the observation error covariance

| xa - xb | = | BHT(HBHT + R)-1 | × | (yo - Hxb) |
| --- | --- | --- | --- | --- |
| Analysis Increment | = | Kalman Gain Matrix | × | Observation minus Forecast |

- The analysis xa is determined from the background xb and the observations yo by:

## Application of Inverse Models, with  Development of a New Method   

- Traditional inversion methods use concentrations to infer sources
- Postulation is that analysis increments (from assimilation) can be used to infer corrections to a-priori sources
- Synthesis inversion modules available (CSU); adjoint methods being developed in GMAO
- Proposing a new method, based on parameter estimation and multi-member ensembles, to minimize impacts of sub-grid transport error - use trajectories with embedded cloud model and observed clouds, eventually full GCM framework

## Transport Uncertainty: Radon using GEOS-4 and -5 Convection

- 	Dashed line: GEOS-4; Solid line: GEOS-4 with GEOS-5-like convection

## Schematic of the Challenges Posed by Uncertainty in Sub-Grid Transport

- 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

**Notes:**
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, ...

## Re-Examination of Parameters in Land and Ocean Models   

- Key sensititivies will be examined to reconcile the corrected fluxes with the original estimates:
- Ocean:  key factors are surface T, net biological uptake, upwelling and wind speed (WHOI, GMAO)
- Land: uncertainties in soil respiration, photosynthesis/ respiration, etc., need to be examined (CSU, GSFC)
- The underlying issue is whether we can reconcile top-down and bottom up estimates, within the error bounds that we understand, and how we may constrain parameters based on all of the information we have

## Science Questions: Stage 1 will give Answers - Maybe Only Partial    

- How well can we constrain carbon fluxes using bottom-up and top-down methods?
- Can we combine in-situ and space-borne data to constrain carbon fluxes over North America?
- How does transport error manifest in the assimilation-inversion process and how may we minimize its impacts on surface flux estimates?
- With what spatio-temporal resolution can we constrain surface fluxes over the globe?
- What is the pattern of the seasonal cycle in global CO2 fluxes and can it be used to identify mechanisms controlling the carbon budget?

## Stage 2: Optimization with Constraints from Above and Below   

- Develop a coupled assimilation system:
- Optimize fluxes using land, ocean and atmosphere data
- Account for the various timescales involved
- Propose an ensemble approach to the assimilation-flux estimation
- Perform a multi-year analysis using all available data

## Year 1 Activities   

- Implement and test the carbon-cycle modules in GEOS-5 atmospheric GCM
- Run land- and ocean-carbon models with GEOS-5 meteorology to infer sources and sinks
- Atmospheric runs using these:
- GCM simulations to investigate sensitivities to sub-grid transport
- Constrained meteorology (GEOS-5) for top-down flux corrections
- Extension of GEOS-5 assimilation modules to include additional gases; first tests with AIRS radiances
- Begin work on inversion techniques, including multi-trajectory studies constrained by GEOS-5 analyses and observed clouds