Transcript University of Leeds November 20th

The UK Universities contribution to the
analysis of GOSAT L1 and L2 data:
towards a better quantitative
understanding of surface carbon fluxes
Paul Palmer, Michael Barkley, Peter Bernath, Hartmut Bösch,
Martyn Chipperfield, Liang Feng, Manuel Gloor, Paul Monks,
Marko Scholze, Parvadha Suntharalingam, and Martin Wooster
Project overview: an integrative approach
WP1 Calibration/validation
activities:
•Models of land-surface exchange
and atmospheric transport
•Comparison with TCCON FTS
•ACE FTS CH4 and CO2 profiles
•AMAZONICA aircraft and surface
measurements of CO2 and CH4
WP3 Surface flux
inversions:
•4 models: GEOS-Chem,
TOMCAT, TM3, and
MOZART
•3 approaches: 4DVAR,
batch-mode KF, and
EnKf
WP2 Intercomparisons of
space-borne CO2 & CH4 data:
•CO2: OCO, SCIA, AIRS, ACE
•CH4: SCIA, IASI
•Obj 1: to generate long-term
consistent data record
•Obj 2: to generate SWIR/TIR
product
WP4 Improved source
attribution:
•Analyse CO2-CO-CH4-OCS
variations.
•OCS: ACE
•CO: TES, AIRS, SCIA, ACE
WP5 Pyroconvection:
•SWIR and TIR ls
sensitive to LT/FT and
FT/UT.
•Link with land-surface
properties (e.g., fire
radiative power) and CFD
fire model
Project overview: an integrative approach
WP1 Calibration/validation
activities:
•Models of land-surface exchange
and atmospheric transport
•Comparison with TCCON FTS
•ACE FTS CH4 and CO2 profiles
•AMAZONICA aircraft and surface
measurements of CO2 and CH4
WP3 Surface flux
inversions:
•4 models: GEOS-Chem,
TOMCAT, TM3, and
MOZART
•3 approaches: 4DVAR,
batch-mode KF, and
EnKf
WP2 Intercomparisons of
space-borne CO2 & CH4 data:
•CO2: OCO, SCIA, AIRS, ACE
•CH4: SCIA, IASI
•Obj 1: to generate long-term
consistent data record
•Obj 2: to generate SWIR/TIR
product
WP4 Improved source
attribution:
•Analyse CO2-CO-CH4-OCS
variations.
•OCS: ACE
•CO: TES, AIRS, SCIA, ACE
WP5 Pyroconvection:
•SWIR and TIR ls
sensitive to LT/FT and
FT/UT.
•Link with land-surface
properties (e.g., fire
radiative power) and CFD
fire model
Unique UK contributions to wider GOSAT cal/val activities
AMAZONICA
(AMAZon Integrated Carbon Analysis)
Troposphere
Greenhouse Gases
Earth
Observations
Greenhouse Gas
Synthesis
Ecosystem
Gas Fluxes
Biomass
Inventories
Aquatic
Carbon
Vegetation
Modelling
ACE instrument
Climate Response
Synthesis of top-down and bottom-up approaches
to better understand major basin-wide CO2, CO,
and CH4 flux processes
ACE CO2 progress (5-25 km):
a) Use ACE temperatures (for now) and
retrieve tangent heights using the N2
continuum.
b) Use selected temperature-insensitive
CO2 lines to get CO2 profiles.
Model studies look promising: Foucher et
al. ACPD (submitted).
Mean ACE-FTS
Mean MIPAS
131
matching
profiles
MIPAS
climatology
De Mazière et al., ACP, 8, 2421 (2008)
Monthly CO2, CO, and CH4 profiles for 48 months
Project overview: an integrative approach
WP1 Calibration/validation
activities:
•Models of land-surface exchange
and atmospheric transport
•Comparison with TCCON FTS
•ACE FTS CH4 and CO2 profiles
•AMAZONICA aircraft and surface
measurements of CO2 and CH4
WP3 Surface flux
inversions:
•4 models: GEOS-Chem,
TOMCAT, TM3, and
MOZART
•3 approaches: 4DVAR,
batch-mode KF, and
EnKf
WP2 Intercomparisons of
space-borne CO2 & CH4 data:
•CO2: OCO, SCIA, AIRS, ACE
•CH4: SCIA, IASI
•Obj 1: to generate long-term
consistent data record
•Obj 2: to generate SWIR/TIR
product
WP4 Improved source
attribution:
•Analyse CO2-CO-CH4-OCS
variations.
•OCS: ACE
•CO: TES, AIRS, SCIA, ACE
WP5 Pyroconvection:
•SWIR and TIR ls
sensitive to LT/FT and
FT/UT.
•Link with land-surface
properties (e.g., fire
radiative power) and CFD
fire model
Generating a consistent space-borne record of CO2 and CH4
SCIAMACHY
AIRS
GOSAT
OCO
Objective: Generate consistent multi-sensor CO2 and CH4 datasets to obtain:
1)
Much denser spatial and temporal sampling for source/sink estimation
2)
Long-term data records
Specific Tasks: Intercomparison of satellite products to identify, characterize
and remove biases in the data products:
1) Detailed comparison of CO2 retrievals from GOSAT and OCO:
- Characterization of retrieval differences with simulations, spectra from
collocated soundings and comparisons to common validation site.
2) Comparison of CO2 and CH4 products to operational and U. Leicester
products retrieved from to SCIAMACHY, AIRS and IASI
Project overview: an integrative approach
WP1 Calibration/validation
activities:
•Models of land-surface exchange
and atmospheric transport
•Comparison with TCCON FTS
•ACE FTS CH4 and CO2 profiles
•AMAZONICA aircraft and surface
measurements of CO2 and CH4
WP3 Surface flux
inversions:
•4 models: GEOS-Chem,
TOMCAT, TM3, and
MOZART
•3 approaches: 4DVAR,
batch-mode KF, and
EnKf
WP2 Intercomparisons of
space-borne CO2 & CH4 data:
•CO2: OCO, SCIA, AIRS, ACE
•CH4: SCIA, IASI
•Obj 1: to generate long-term
consistent data record
•Obj 2: to generate SWIR/TIR
product
WP4 Improved source
attribution:
•Analyse CO2-CO-CH4-OCS
variations.
•OCS: ACE
•CO: TES, AIRS, SCIA, ACE
WP5 Pyroconvection:
•SWIR and TIR ls
sensitive to LT/FT and
FT/UT.
•Link with land-surface
properties (e.g., fire
radiative power) and CFD
fire model
Progress of flux estimation studies depends on WP1 & WP2
UKMO NAME @ Leicester
a
  1 f

Jan - Feb
Small
Large
BM KF
EnKF
GEOS-Chem
X
X
MOZART
X
TM3
TOMCAT
4DVar
X
X
X
•Regional flux estimation
will use the NAME
Lagrangian model
•Focus on (a) wildfires
and (b) metropolitan
areas over Europe
Project overview: an integrative approach
WP1 Calibration/validation
activities:
•Models of land-surface exchange
and atmospheric transport
•Comparison with TCCON FTS
•ACE FTS CH4 and CO2 profiles
•AMAZONICA aircraft and surface
measurements of CO2 and CH4
WP3 Surface flux
inversions:
•4 models: GEOS-Chem,
TOMCAT, TM3, and
MOZART
•3 approaches: 4DVAR,
batch-mode KF, and
EnKf
WP2 Intercomparisons of
space-borne CO2 & CH4 data:
•CO2: OCO, SCIA, AIRS, ACE
•CH4: SCIA, IASI
•Obj 1: to generate long-term
consistent data record
•Obj 2: to generate SWIR/TIR
product
WP4 Improved source
attribution:
•Analyse CO2-CO-CH4-OCS
variations.
•OCS: ACE
•CO: TES, AIRS, SCIA, ACE
WP5 Pyroconvection:
•SWIR and TIR ls
sensitive to LT/FT and
FT/UT.
•Link with land-surface
properties (e.g., fire
radiative power) and CFD
fire model
Interpreting GOSAT CO2 data using CO, COS, and CH4 data will
improve CO2 source attribution
CO, CH4, VOCs, NOx,
HCN, H2
CO2
COS, VOCs
BIG IDEA: Observed correlations between CO2 and other species arise
from common sources, source regions and atmospheric transport
Objective: Develop multiple-species inverse analysis framework to
incorporate remote sensing measurements using 3-D atmospheric
model simulations
Project overview: an integrative approach
WP1 Calibration/validation
activities:
•Models of land-surface exchange
and atmospheric transport
•Comparison with TCCON FTS
•ACE FTS CH4 and CO2 profiles
•AMAZONICA aircraft and surface
measurements of CO2 and CH4
WP3 Surface flux
inversions:
•4 models: GEOS-Chem,
TOMCAT, TM3, and
MOZART
•3 approaches: 4DVAR,
batch-mode KF, and
EnKf
WP2 Intercomparisons of
space-borne CO2 & CH4 data:
•CO2: OCO, SCIA, AIRS, ACE
•CH4: SCIA, IASI
•Obj 1: to generate long-term
consistent data record
•Obj 2: to generate SWIR/TIR
product
WP4 Improved source
attribution:
•Analyse CO2-CO-CH4-OCS
variations.
•OCS: ACE
•CO: TES, AIRS, SCIA, ACE
WP5 Pyroconvection:
•SWIR and TIR ls
sensitive to LT/FT and
FT/UT.
•Link with land-surface
properties (e.g., fire
radiative power) and CFD
fire model
Large uncertainties in the distribution, magnitude and vertical
transport of biomass burning emissions
Active fire pixel, coloured by date of detection
Sahel
1
3
6
9
14
Day of Feb 2004
Meteosat Imaging Disk
04/02/04 – 14/02/04
Deciduous woodland
Deciduous shrubland
Savanna
Cropland
Rate of biomass combustion [kg/sec]
Fire radiative power [MW]
(15 mins temporal resl.)
BIG IDEA: use FRP with GOSAT
NIR/TIR CO2 measurements to
quantify the influence of
biomass burning on the vertical
profile of CO2.
Method: Gonzi and Palmer, submitted,
2008 (example using CO)