Using High-Resolution Forward Model Simulations of Ideal Atmospheric Tracers

to Assess the Spatial Information Content of Inverse CO2 Flux Estimates

Steven Pawson and J. Eric Nielsen*Global Modeling and Assimilation Office

NASA Goddard Space Flight Center* Science Systems and Applications Inc.

Funding: NASA’s Modeling, Analysis and Prediction (MAP) ProgramComputing: NASA’s High-End Computing resources at NASA GSFC/NCCS

AGU 2010 Fall Meeting – San Francisco, CA – Session A54D – December 17, 2010

Relating CO2 Concentrations to Fluxes

Surface fluxes (Emissions and

Uptake)

Surface fluxes (Emissions and

Uptake)

Atmospheric Observations

(Concentrations)

Atmospheric Observations

(Concentrations)Transport

Model

Global surface fluxes: •Inventories (FF, BF)•“Constrained” model estimates (biological, biomass burning, ocean uptake)

Global Atmospheric Observations: •Sparse surface networks•More dense UT estimates from satellites•Emerging total column from satellites (land bias)

This talk is about using a forward transport model to infer information about inverse approaches (top-down flux estimates):

specifically, what spatial scales of sources can be resolved

This talk is about using a forward transport model to infer information about inverse approaches (top-down flux estimates):

specifically, what spatial scales of sources can be resolved

Methodology Grid of Idealized Tracers

Idealized tracers (constant source strength) are continuously emitted in the shaded grid boxes

Methodology Simple Tracers in a Complex Model

Component ConfigurationGEOS-5 AGCM 0.5°×0.625°L72 with “full” physicsIdealized Tracers 48-member grid over Eastern North AmericaTracer Grid Every 2.5° of longitude & every 2° of latitudeEmissions Continuous emissions (1kgm-2s-1) for 10 daysInitialization Concentrations zero at start of each 10-day

period

Analysis focuses on surface concentrations and layer averages for total column (TC), mid-troposphere (MT: 500-300hPa) and upper troposphere (UT: 300-100hPa)

Growth of Plumes with TimeSurface Concentration (sum of nine boxes) and mean sea-level pressure in Trial 9

(color scale is logarithmic)

Day 2 Day 8

Growth of Plumes over 10-Day Trial

Trial 9: 19-28 FebruaryArea with column-integrated mass larger than 10 kgm-2

All 48 tracers shownColor reflects origin – not important here

This trial is fairly typical of all 36 cases (summer and winter)

Area Overlap Ratio: Definition

0 < Rmn = Am&n / Am < 1The fraction of observations of Tracer ‘m’ that are contaminated by Tracer ‘n’

Am An

A m&n

Rmn = 1 An observation contains no unique information about source regions m and n – inverse modeling can infer only the total source of m+n

Rmn = 0Observations of Tracer ‘m’ are uncontaminated by Tracer ‘n’

Adjacent Sources: Large Overlap

Adjacent Sources: Large Overlap

More distant sources: small overlap

More distant sources: small overlap

Area Overlap Ratio (Rm,n): Trial 9, Day 1

Area Overlap Ratio (Rm,n): Trial 9, Day 8

R20,n: Tracer 20, Trial 9 – after 2 Days

R20,n: Tracer 20, Trial 9 – after 8 Days

R20,n: Tracer 20, Trial 25 – after 8 Days

Mid-Troposphere: Four Trials, Day 8Trial 8Winter

Trial 9Winter

Trial 24Summer

Trial 25Summer

Summary and Further WorkAt 0.5° model resolution, downstream total-column observations seem unable to isolate sources with less than 10° of separation

Flow dependence is large – intra-seasonal variations are as big as inter-seasonal differences, despite contrasting transport mechanisms

Need more Trials to build full statistics (e.g., co-variances) of these simple cases – also a bigger grid would be helpful!

Planning experimentation with tagged CO2 sources and pseudo-observations to complement these idealized cases (OSSEs)

Intended to enhance understanding of “top-down” flux estimation – “inverse” techniques likely increase smearing of signals

Transport uncertainty (advection core and sub-grid) is another aspect that will be investigated