CAMS greenhouse gas fluxes
Frédéric Chevallier,
Sander Houweling,
Arjo Segers,
Rona Thompson
Atmosphere Monitoring
Atmospheric meas. of
long-lived GHGs
Atmosphere
Monitoring
Concentration measurements
at Amsterdam Island, F.
Source: LSCE/RAMCES
o CO2, CH4 and N2O are the three main ones, with major contributions to climate
change.
o Decadal trends + shorter temporal variations + spatial variations.
o What are the drivers of the underlying sources and sinks of these gases?
o Such information can be provided by models (e.g., CAMS-41) and inventories
(fires in CAMS-44, anthropogenic and natural emissions in CAMS-81).
o It also lies in the space-time gradients of the concentrations and can be
extracted by “atmospheric inversion systems” (CAMS-73).
Two
variational inversion systems for CAMS
Atmosphere
Monitoring
o
o
Direct heritage of GEMS/MACC with inspiration from ECMWF’s 4D-Var.
CO2 (CEA) and N2O (NILU + CEA):
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o
PYVAR system,
Includes ECMWF’s congrad and INRIA’s M1QN3,
Includes LMDZ transport model (mass fluxes computed from a full General Circulation
Model guided by ECMWF winds),
Global 3.75o × 1.9o × 39 layers.
CH4 (TNO + SRON):
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–
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TM5-4DVAR system,
Includes ECMWF’s congrad and INRIA’s M1QN3,
Includes TM5 transport model (mass fluxes diagnosed from the ECMWF re-analysis),
Global 6o × 4o × 25 layers and 3o × 2o × 34 layers.
Current CAMS inversion shop-window
Atmosphere
Monitoring
Associated documentation:
1 “ATBD” and 1 “PVIR” for
each product stream.
+ a series of service evolution
studies. In 2016, they were:
 (CO2) Use of OCO-2 data.
 (CO2) Evaluation of country-scale numbers.
 (N2O) Changes to the error covariance calculations.
 (CH4) Changes to TM5.
 (CH4) Changes to the satellite bias correction.
A
Atmosphere
Monitoring
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long record for the CO
v10r1: released in August 2011
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2000-2010
v10r2: released in Feb 2012
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1981-2010
v11r1: released in September 2012
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1979-2011
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Physical parallelization
v11r2: released in May 2013
v12r1: released in October 2013
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1979-2012
v12r2: released in January 2014
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LMDZ4 with 39 layers
v13r1: released in July 2014
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1979-2013
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Distributed through ECMWF server
v14r1: released in May 2015
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1979-2014
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LMDZ5A
v14r2: released in Sep 2015
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1979-2014
v15r2: released in May 2016
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1979-2015
v15r4: released in Nov 2016
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1979-2015
v16r1: planned for June 2017
2
product
1979-2016
Transition to a new supercomputer.
Less cores per socket, less memory per core, faster cores.
Current production uses 266 slave cores, plus “2” masters.
Use annually-varying prior ocean fluxes.
New observation interface with NOAA data.
Replaced 1 corrupted monthly mass flux file.
Fit to un-assimilated data
Atmosphere
Monitoring
•
CAMS XCO2 and OCO-2 XCO2 nadir-mode retrievals vs. TCCON, year 2015
OCO-2 retrievals were produced
by the OCO-2 project at the Jet
Propulsion Laboratory,
California Institute of
Technology, and obtained from
the ACOS/OCO-2 data archive
maintained at the NASA
Goddard Earth Science Data and
Information Services Center.
TCCON data were
obtained from the TCCON
Data Archive, hosted by
the Carbon Dioxide
Information Analysis
Center (CDIAC) at Oak
Ridge National Laboratory,
Oak Ridge, Tennessee,
U.S.A.,
http://tccon.ornl.gov
CO
Atmosphere
Monitoring
2
fluxes at national scale
Data publicly available at grid point scale: are they meaningful when aggregated at national scale?
Case of France:
• 2001-2010:
– v15r2 = 70 ± 117 MtCO2/yr
– National forestry inventory = 118 MtCO2/yr
• Interannual variability linked to large-scale climate patterns over the Atlantic (Bastos et al., 2016).
– But edge effect is visible in v15r2 (stops in Dec 2015) vs. v15r4 (stops in June 2016).
v15r2
v15r4
Total natural flux (CAMS) ± 1σ
Land use, land use change and forestry (UNFCCC)
Fossil fuel (UNFCCC)
CH
4
production chain
Atmosphere
Monitoring
Simulating CH4 requires proper emissions and initial concentrations
Two-step chain:
"A" : sequential series of inversions to obtain initial conc. (1-year windows, resolution 6o×4o, 25 layers)
"B" : parallel inversions on target resolution
Based on MACC inversion system by P. Bergamaschi (JRC):
(3-year windows, resolution 3o × 2o, 34 layers)
• 4D-var method
• Estimate emissions in 4 categories,
wetlands, rice, biomass burning, other (anthrop.)
• TM5 transport model
• ERA-Interim meteo
• 6-month spin-up/down
"Stream 1" using NOAA surface observations only
"Stream 2" using NOAA surface observations
and GOSAT XCH4 columns (2009-...)
Latest CH
Atmosphere
Monitoring
•
4
release
v11r1 : re-analysis 2000-2015 using NOAA surf. obs.
– Higher wetland and rice emissions than prior in NH summer/autumn
– Lower “other” (anthropog.) emissions than prior in summer, especially in
East Asia
– Comparison to aircraft data in the free troposphere: |b| < 5 ppb, σ ~ 15 ppb
– Comparison to TCCON: persistent north-south bias
wetlands
Bias (obs - posterior)
rice
biomass burning
•
v12r1 : re-analysis 2009-2015 using NOAA surf. obs. and GOSAT XCH4
Being re-run with better configuration and retrieval biascorrection
"other" (anthrop.)
Service evolution for
Atmosphere
Monitoring
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CH
4
Improvement of the consistency between tracer transport as simulated
in IFS and TM5 => Phase 1: Diagnosing differences
Improvement of the treatment of GOSAT biases => Phase 1:
Transport model induced biases & comparison to TCCON / AirCore
Improvement of the computational efficiency of multi-year inversions
=> Phase 1: Chevallier et al, GMD, 2013 approach applied to CH4
Preparation of TM5-4DVAR for the use of Sentinel-5P XCH4 retrievals
t=1 year
IFS
TM5
Next steps in the IFS/TM5 comparison:
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Use ERA-Interim diffusive mass fluxes in TM5
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Diffusion thresholds in the TM5 stratosphere
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Different mass fixer options in IFS
TM5 - IFS
M a i n r e s e a r c h q u e s t i o n s f o r N 2O
Atmosphere
Monitoring
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Where are the major source regions and hotspots?
How are N2O emissions varying in time?
Are anthropogenic emissions increasing or decreasing?
What drives year-to-year variations in emissions?
How important is stratosphere-troposphere transport for variation in tropospheric N2O?
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127 sites used in the CAMS N2O inversion to address them.
They have a large impact on the posterior estimate
E a s t - A s i a n N 2O
Atmosphere
Monitoring
•
emission trends
Increasing N2O emissions owing to more Nfertilizer use and lower Nitrogen Use Efficiency (NUE)
E u r o p e a n N 2O
Atmosphere
Monitoring
•
emission trends
Decreasing N2O emissions since 2003 owing to less N-fertilizer and higher Nitrogen Use
Efficiency (NUE)
Closing remarks
Atmosphere
Monitoring
o
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Visibility of the CAMS inversions could be further increased.
 Develop web interface when Climate data store is ready.
The Global Carbon Project is our current VIP user.
 CAMS CO2 v15r2 last year.
 MACC-II CH4 last year (lead CEA).
 CAMS N2O v15r1 this year (planned, lead NILU).
Analysis of very recent trends.
 CAMS CO2 v15r3 (intermediate version).
 Market segment to be developed?
Foster interaction with new satellite projects and with future H2020 projects that include GHG
atmospheric inversions.
Expand the service towards other species?
Acknowledgements
Atmosphere
Monitoring
CAMS-73 is very grateful to the many people
involved in the surface and aircraft CO2, CH4 and
N2O measurements and in the archiving of these
data that are kindly made available to them by
various means.