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