Validation report for the
inverted CO2 fluxes, v15r2
version 1.1
Issued by: CEA
Date: 04/07/2016
REF.: CAMS73_2015S1_ D73.1.2_201606
Copernicus Atmosphere Monitoring Service
Copernicus Atmosphere Monitoring Service
This document has been produced in the context of the Copernicus Atmosphere Monitoring
Service (CAMS). The activities leading to these results have been contracted by the
European Centre for Medium-Range Weather Forecasts, operator of CAMS on behalf of the
European Union (Delegation Agreement signed on 11/11/2014). All information in this
document is provided "as is" and no guarantee or warranty is given that the information is
fit for any particular purpose. The user thereof uses the information at its sole risk and
liability. For the avoidance of all doubts, the European Commission and the European Centre
for Medium-Range Weather Forecasts has no liability in respect of this document, which is
merely representing the authors view.
Validation report for the inverted CO2 fluxes, v15r2|
Copernicus Atmosphere Monitoring Service
Copernicus Atmosphere Monitoring Service
Validation report for the
inverted CO2 fluxes, v15r2
version 1.1
CEA (Frédéric Chevallier)
Date: 04/07/2016
REF.: CAMS73_2015S1_ D73.1.2_201606
Validation report for the inverted CO2 fluxes, v15r2|
Copernicus Atmosphere Monitoring Service
Copernicus Atmosphere Monitoring Service
Contents:
1 Introduction .................................................................................................... 2
2 Inversion configuration ..................................................................................... 2
3 Evaluation ....................................................................................................... 7
3.1 Benchmarking using a poor man’s inversion .................................................. 7
3.2 Fit to the assimilated measurements ............................................................ 8
3.3 Fit to the independent measurements ........................................................... 8
Appendix A: Time series of the fit to the dependent surface measurements ............. 12
Appendix B: Time series of the fit to the independent measurements ...................... 24
Validation report for the inverted CO2 fluxes, v15r2|
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1 Introduction
The inversion system that generates the CAMS global CO2 atmospheric inversion
product is called PYVAR. It has been initiated, developed and maintained at CEA/LSCE
within the series of precursor projects GEMS/MACC/MACC-II/MACC-III (Chevallier
2016, and references therein). Here, we synthesize the evaluation of version 15r2 that
was released in May 2016. Section 2 describes the PYVAR-CO2 configuration that was
used and Section 3 presents the evaluation synthesis.
2 Inversion configuration
The transport model in PyVAR-CO2 is the global general circulation model LMDZ in its
version LMDZ5A (Locatelli et al. 2015), that uses the deep convection model of
Emanuel (1991). This version corresponds to the one developed and used for the fifth
phase of the Coupled Model Inter-comparison Project (CMIP5). Horizontal winds are
nudged to the winds analysed by ECMWF, and the transport mass fluxes are computed
once and for all, before being used off-line for tracer transport. This version has a
regular horizontal resolution of 3.75o in longitude and 1.875o in latitude, with 39 hybrid
layers in the vertical.
The inferred fluxes are estimated in each horizontal grid point of the transport model
with a temporal resolution of 8 days, separately for day-time and night-time. The state
vector of the inversion system is therefore made of a succession of global maps with
9,200 grid points. Per month it gathers 73,700 variables (four day-time maps and four
night-time maps). It also includes a map of the total CO2 columns at the initial time
step of the inversion window in order to account for the uncertainty in the initial state
of CO2.
The prior values of the fluxes combine estimates of (i) gridded annual anthropogenic
emissions (EC-JRC/PBL EDGAR version 4.2, and CDIAC), climatological monthly ocean
fluxes, (Takahashi et al. 2009), monthly biomass burning emissions (GFED 4.1s until
2014 and GFAS for 2015) and climatological 3-hourly biosphere-atmosphere fluxes
taken as the 1989-2010 mean of a simulation of the ORganizing Carbon and Hydrology
In Dynamic EcosystEms model (ORCHIDEE, Krinner et al. 2005), version 1.9.5.2. The
mass of carbon emitted annually during specific fire events is compensated here by the
same annual flux of opposite sign representing the re-growth of burnt vegetation,
which is distributed regularly throughout the year. The gridded prior fluxes exhibit 3hourly variations but their inter-annual variations are only caused by anthropogenic
emissions. This feature was explicitly demanded by some users who wanted the
interannual signals in the inverted natural fluxes to be strictly driven by the
atmospheric measurements.
Over land, the errors of the prior biosphere-atmosphere fluxes are assumed to
dominate the error budget and the covariances are constrained by an analysis of
mismatches with in situ flux measurements (Chevallier et al. 2006, 2012): temporal
correlations on daily mean Net Carbon Exchange (NEE) errors decay exponentially with
a length of one month but night-time errors are assumed to be uncorrelated with
daytime errors; spatial correlations decay exponentially with a length of 500 km;
standard deviations are set to 0.8 times the climatological daily-varying heterotrophic
respiration flux simulated by ORCHIDEE with a ceiling of 4 gC∙m-2 per day. Over a full
Validation report for the inverted CO2 fluxes, v15r2|
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year, the total 1-sigma uncertainty for the prior land fluxes amounts to about 3.0
GtC∙yr-1. The error statistics for the open ocean correspond to a global air-sea flux
uncertainty about 0.5 GtC∙yr-1 and are defined as follows: temporal correlations decay
exponentially with a length of one month; unlike land, daytime and night-time flux
errors are fully correlated; spatial correlations follow an e-folding length of 1000 km;
standard deviations are set to 0.1 gC∙m-2 per day. Land and ocean flux errors are not
correlated.
Observation uncertainty in the inversion system is dominated by uncertainty in
transport modelling and is represented from the variance of the high frequency
variability of the de-seasonalized and de-trended CO2 time series of the measurement
at a given location.
Version 15r2 analysed 37 years of surface measurements, from 1979 to 2015 in a
single data assimilation window. The assimilated measurements are surface air-sample
measurements of the CO2 dry air mole fraction made in 133 sites over the globe. The
detailed list of sites is provided in Tables 1 and 2 and their location is displayed per
year in Figure 1. The irregular space-time density of the measurements implies a
variable constraint on the inversion throughout the 37 years, which is documented by
the associated Bayesian error statistics.
Figure 1: Location of the assimilated measurements over the globe for each year in v15r2.
Validation report for the inverted CO2 fluxes, v15r2|
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Locality (indentifier)
Period
Source
Alert, Nunavut, CA (ALT)
1988-2014
WDCGG/ EC
Amsterdam Island, FR (AMS)
1981-2011
LSCE
Amsterdam Island, FR (AMS)
2012-2015
ICOS/ LSCE
Argyle, Maine, US (AMT)
2003-2015
NOAA/ ESRL
Anmyeon-do, KR (AMY)
1999-2014
WDCGG/ KMA
Barrow, Alaska, US (BRW)
1979-2015
NOAA/ ESRL
Candle Lake, CA (CDL)
Centro de Investigacion de la Baja
Atmosfera, ES (CIB)
2002-2012
WDCGG/ EC
2009-2015
NOAA/ ESRL
Monte Cimone, IT (CMN)
1996-2014
WDCGG/ IAFMS
Cape Ochi-ishi, JP (COI)
1995-2002
WDCGG/ NIES
Cape Point, SA (CPT)
1993-2014
WDCGG/ SAWS
Egbert, CA (EGB)
Estevan Point, British Columbia, CA
(ESP)
2005-2014
WDCGG/ EC
2009-2014
WDCGG/ EC
East Trout Lake, CA (ETL)
2005-2014
WDCGG/ EC
Frasedale, CA (FSD)
1990-2014
WDCGG/ EC
Hateruma, JP (HAT)
Hegyhatsal tower, 115m level, HU
(HUN0115)
1993-2002
WDCGG/ NIES
1994-2014
WDCGG/ HMS
Ivittuut, Greenland, DK (IVI)
2011-2014
ICOS/ LSCE
Tenerife, Canary Islands, ES (IZO)
1984-2015
WDCGG/ AEMET
Jubany, Antartica, AR (JBN)
1994-2009
WDCGG/ ISAC IAA
Jungfraujoch, CH (JFJ)
2004-2014
WDCGG/ Univ. Of Bern
K-puszta, HU (KPS)
1981-1999
WDCGG/ HMS
Park Falls, Wisconsin, US (LEF)
2003-2015
NOAA/ ESRL
WDCGG/ EC
Lac La Biche, Alberta, CA (LLB)
2007-2014
Mace Head, County Galway, IE (MHD)
1992-2009
LSCE
Mace Head, County Galway, IE (MHD)
2010-2015
ICOS/ LSCE
Mauna Loa, Hawaii, US (MLO)
1979-2015
NOAA/ ESRL
Minamitorishima, JP (MNM)
1993-2014
WDCGG/ JMA
Neuglobsow, DE (NGL)
Pallas-Sammaltunturi, GAW Station, FI
(PAL)
1994-2013
WDCGG/ UBA
1999-2014
WDCGG/ FMI
WDCGG/ CESI RICERCA
Plateau Rosa, IT (PRS)
2000-2014
Puy de Dome, FR (PUY)
2000-2010
LSCE
Puy de Dome, FR (PUY)
2011-2014
ICOS/ LSCE
Ryori, JP (RYO)
1987-2015
WDCGG/ JMA
Tutuila, American Samoa (SMO)
1979-2015
NOAA/ ESRL
Sonnblick, AU (SNB)
1999-2014
WDCGG/ EEA
South Pole, Antarctica, US (SPO)
1979-2015
NOAA/ ESRL
Westerland, DE (WES)
1979-2013
WDCGG/ UBA
Moody, Texas, US (WKT)
2003-2015
NOAAA/ ESRL
Sable Island, CA (WSA)
1992-2014
WDCGG/ EC
Yonagunijima, JP (YON)
1997-2015
WDCGG/ JMA
Table 1: List of the continuous sites used in v15r2 together with the period of coverage (defined as the period
between the first sample and the last one), and the data source. Each station is identified by the name of the
place, the corresponding country (abbreviated) and the code used in the corresponding database.
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Locality (indentifier)
Period
Source
Alert, Nunavut, CA (ALT)
1985-2015
NOAA/ ESRL
Alert, Nunavut, CA (ALT)
1979-2014
WDCGG/ EC
Alert, Nunavut, CA (ALT)
1991-2014
WDCGG/ CSIRO
Amsterdam Island, FR (AMS)
1979-1990
NOAA/ ESRL
Amsterdam Island, FR (AMS)
2003-2015
LSCE
Ascension Island, GB (ASC)
1979-2015
NOAA/ ESRL
Assekrem, DZ (ASK)
1995-2015
NOAA/ ESRL
St. Croix, Virgin Islands, USA (AVI)
1979-1990
NOAA/ ESRL
Terceira Island, Azores, PT (AZR)
1979-2015
NOAA/ ESRL
Baltic Sea, PL (BAL)
1992-2011
NOAA/ ESRL
Bering Island, RU (BER)
1986-1994
WDCGG/ MGO
Begur, ES (BGU)
2000-2014
LSCE/ IC·3
Baring Head, NZ (BHD)
1999-2015
NOAA/ESRL
Baring Head, NZ (BHD)
1979-2014
WDCGG/ NIWA
St. Davids Head, Bermuda, GB (BME)
1989-2009
NOAA/ ESRL
Tudor Hill, Bermuda, GB (BMW)
1989-2015
NOAA/ ESRL
Barrow, Alaska, US (BRW)
1979-2015
NOAA/ ESRL
Cold Bay, Alaska, US (CBA)
1979-2015
NOAA/ ESRL
Cape Ferguson, AU (CFA)
1991-2014
WDCGG/ CSIRO
Cape Grim, Tasmania, AU (CGO)
1984-2015
NOAA/ ESRL
Churchill, CA (CHL)
Christmas Island, Republic of Kiribati
(CHR)
2007-2014
WDCGG/ EC
1984-2014
NOAA/ ESRL
Cape Meares, Oregon, US (CMO)
1982-1998
NOAA/ ESRL
Crozet Island, FR (CRZ)
1991-2015
NOAA/ ESRL
Cape St. James, CA (CSJ)
1979-1992
WDCGG/ EC
Casey Station, AU (CYA)
Drake Passage (DRP)
1996-2013
2003-2015
WDCGG/ CSIRO
Easter Island, CL (EIC)
Estevan Point, British Columbia, CA
(ESP)
Estevan Point, British Columbia, CA
(ESP)
1994-2015
NOAA/ ESRL
1992-2014
WDCGG/ EC
1993-2001
WDCGG/ CSIRO
Finokalia, Crete, GR (FIK)
1999-2015
LSCE
NOAA/ ESRL
Mariana Islands, Guam (GMI)
1979-2015
NOAA/ ESRL
Dwejra Point, Gozo, MT (GOZ)
1993-1998
NOAA/ ESRL
Halley Station, Antarctica, GB (HBA)
1983-2014
NOAA/ ESRL
Hanle, IN (HLE)
2000-2013
LSCE
Hohenpeissenberg, DE (HPB)
2006-2015
NOAA/ ESRL
Humboldt State University, US (HSU)
2008-2014
NOAA/ ESRL
Hegyhatsal, HU (HUN)
1993-2015
NOAA/ ESRL
Storhofdi, Vestmannaeyjar, IS (ICE)
1992-2015
NOAA/ ESRL
Grifton, North Carolina, US (ITN)
1992-1999
WDCGG/ ESRL
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Ivittuut, Greenland, DK (IVI)
2007-2014
LSCE
Tenerife, Canary Islands, ES (IZO)
1991-2015
NOAA/ ESRL
Key Biscayne, Florida, US (KEY)
1979-2015
NOAA/ ESRL
Kotelny Island, RU (KOT)
1986-1993
WDCGG/ MGO
Cape Kumukahi, Hawaii, US (KUM)
1979-2015
NOAA/ ESRL
Sary Taukum, KZ (KZD)
1997-2009
NOAA/ ESRL
Plateau Assy, KZ (KZM)
1997-2009
NOAA/ ESRL
Lac La Biche, Alberta, CA (LLB)
2008-2014
NOAA/ ESRL
Lulin,
2006-2014
NOAA/ ESRL
Lampedusa, IT (LMP)
TW (LLN)
2006-2015
NOAA/ ESRL
Ile grande, FR (LPO)
2004-2013
LSCE
Mawson, AU (MAA)
1990-2014
WDCGG/ CSIRO
Mould Bay, Nunavut, CA (MBC)
1980-1997
NOAA/ ESRL
High Altitude GCOC, Mexico (MEX)
2009-2015
NOAA/ ESRL
Mace Head, County Galway, IE (MHD)
1991-2015
NOAA/ ESRL
Mace Head, County Galway, IE (MHD)
1996-2015
LSCE
Sand Island, Midway, US (MID)
1985-2015
NOAA/ ESRL
Mt. Kenya, KE (MKN)
2003-2011
NOAA/ ESRL
Mauna Loa, Hawaii, US (MLO)
1979-2015
NOAA/ ESRL
Macquarie Island, AU (MQA)
Farol De Mae Luiza Lighthouse, BR
(NAT)
1990-2014
WDCGG/ CSIRO
2011-2015
NOAA/ ESRL
Gobabeb, NA (NMB)
1997-2015
NOAA/ ESRL
Niwot Ridge, Colorado, US (NWR)
1979-2015
NOAA/ ESRL
Olympic Peninsula, WA, USA (OPW)
1984-1990
NOAA/ ESRL
Ochsenkopf, DE (OXK)
Pallas-Sammaltunturi, GAW Station, FI
(PAL)
2003-2015
NOAA/ ESRL
2001-2015
NOAA/ ESRL
Pic du Midi, FR (PDM)
2001-2015
LSCE
Pacific Ocean, 0N (POC000)
1987-2015
NOAA/ ESRL
Pacific Ocean, 5N (POCN05)
1987-2015
NOAA/ ESRL
Pacific Ocean, 10N (POCN10)
1987-2015
NOAA/ ESRL
Pacific Ocean, 15N (POCN15)
1987-2015
NOAA/ ESRL
Pacific Ocean, 20N (POCN20)
1987-2015
NOAA/ ESRL
Pacific Ocean, 25N (POCN25)
1987-2015
NOAA/ ESRL
Pacific Ocean, 30N (POCN30)
1987-2015
NOAA/ ESRL
Pacific Ocean, 5S (POCS05)
1987-2015
NOAA/ ESRL
Pacific Ocean, 10S (POCS10)
1987-2015
NOAA/ ESRL
Pacific Ocean, 15S (POCS15)
1987-2015
NOAA/ ESRL
Pacific Ocean, 20S (POCS20)
1987-2015
NOAA/ ESRL
Pacific Ocean, 25S (POCS25)
1987-2015
NOAA/ ESRL
Pacific Ocean, 30S (POCS30)
1987-2015
NOAA/ ESRL
Pacific Ocean, 35S (POCS35)
1987-2015
NOAA/ ESRL
Palmer Station, Antarctica, US (PSA)
1979-2015
NOAA/ ESRL
Point Arena, California, US (PTA)
1999-2011
NOAA/ ESRL
Puy de Dome, FR (PUY)
2001-2015
LSCE
Ragged Point, BB (RPB)
1987-2015
NOAA/ ESRL
South China Sea, 3N (SCSN03)
1991-1998
NOAA/ ESRL
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South China Sea, 6N (SCSN06)
1991-1998
NOAA/ ESRL
South China Sea, 9N (SCSN09)
1991-1998
NOAA/ ESRL
South China Sea, 12N (SCSN12)
1991-1998
NOAA/ ESRL
South China Sea, 15N (SCSN15)
1991-1998
NOAA/ ESRL
South China Sea, 18N (SCSN18)
1991-1998
NOAA/ ESRL
South China Sea, 21N (SCSN21)
1991-1998
NOAA/ ESRL
Shangdianzi, CN (SDZ)
2009-2014
NOAA/ ESRL
Mahe Island, SC (SEY)
Southern Great Plains, Oklahoma, US
(SGP)
Shemya Island, Alaska,
US (SHM)
Ship between Ishigaki Island and
Hateruma Island, JP (SIH)
1980-2015
NOAA/ ESRL
2002-2015
NOAA/ ESRL
1985-2015
1993-2005
NOAA/ ESRL
WDCGG/ Tohoku
University
Shetland, Scotland, GB (SIS)
1992-2003
WDCGG/ CSIRO
Tutuila, American Samoa (SMO)
1979-2015
NOAA/ ESRL
South Pole, Antarctica, US (SPO)
1979-2015
NOAA/ ESRL
Ocean Station M, NO (STM)
1980-2009
NOAA/ ESRL
Summit, GL (SUM)
1997-2015
NOAA/ ESRL
Syowa Station, Antarctica, JP (SYO)
1986-2014
NOAA/ ESRL
Tae-ahn Peninsula, KR (TAP)
1991-2015
NOAA/ ESRL
Trinidad Head, California, US (THD)
2002-2015
NOAA/ ESRL
Trainou 180m agl, FR (TR3)
2006-2015
LSCE
Tromelin Island, F (TRM)
1998-2007
LSCE
Tierra Del Fuego, Ushuaia, AR (USH)
1994-2015
NOAA/ ESRL
Wendover, Utah, US (UTA)
1993-2015
NOAA/ ESRL
Ulaan Uul, MN (UUM)
1992-2015
NOAA/ ESRL
Sede Boker, Negev Desert, IL (WIS)
1995-2015
NOAA/ ESRL
Mt. Waliguan, CN (WLG)
1990-2015
NOAA/ ESRL
Sable Island, CA (WSA)
1979-2014
WDCGG/ EC
Western Pacific Cruise (WPC)
Ny-Alesund, Svalbard, Norway and
Sweden (ZEP)
2004-2013
NOAA/ ESRL
1994-2015
NOAA/ ESRL
Table 2: Same as Table 1 but for the flask-sampling sites.
3 Evaluation
3.1 Benchmarking using a poor man’s inversion
The improvement brought by a flux inversion on the simulation of mole fractions usually
looks impressive because the inversion easily corrects the growth rate of CO2. However,
since the global trend can be accurately obtained from just a few marine surface sites,
like MLO and SPO, it is important to assess whether inverted fluxes actually capture more
information than this trend. In other words, we may wonder whether all the stations
exploited here bring some constraint on the flux distribution that is superior to the global
trend from MLO and SPO. For this purpose, Chevallier et al. (2009) introduced a baseline
inversion that they called Poor man’s inversion, against which more sophisticated
inversions can be benchmarked. In this baseline, the ocean fluxes are kept identical to
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the prior ones. Over land, the poor man’s flux Fpm at location (x,y) and at time t is defined
as:
(1)
Fpm (x,y,t) = Fprior (x,y,t) + k(year)·σ(x,y,t)
Fprior(x,y,t) is the prior flux at the same time and location. σ(x,y,t) is its uncertainty,
i.e. the standard deviation of the prior error described in Section 2. k(year) is a
coefficient that varies as a function of the year only. k is chosen here so that the mean
annual global totals of the poor man’s fluxes equals the mean global totals given by
http://www.esrl.noaa.gov/gmd/ccgg/trends/ multiplied by a conversion factor (2.086
GtC·yr-1 per ppm, from Prather et al. 2012). In practice, this simple approach
distributes the land carbon sink according to the heterotrophic respiration fluxes from
the vegetation without any spatial information from the atmospheric observations, nor
any temporal information within any given year.
3.2 Fit to the assimilated measurements
(a) RMS
(b) normalised RMS
Figure 2: Statistics of the differences between LMDZ simulations and individual surface flask measurements.
The LMDZ simulations use the Poor man’s fluxes (abscissa) or the posterior flux sets as boundary conditions
(ordinate). One point shows (a) the RMS or (b) the RMS normalised by the observation error standard
deviation for the analysis period (1979–2015) at one of the assimilated measurement site.
Figure 2 shows the posterior root mean square difference (RMS) as a function of the
corresponding statistics for the Poor man (except that the small bias of the Poor man is
not accounted for) at each assimilated site for the assimilation period. As expected, the
inversion performs at least as good as the benchmark and usually performs better. As
expected too, the two inversions fit the assimilated data within the assigned standard
deviation of the observation uncertainty, which the Poor man’s fluxes do not do.
The time series of measurements and posterior simulation at each station are
reproduced in Appendix A.
3.3 Fit to the independent measurements
Comparisons are also made with independent dry air mole fraction measurements. We
define five datasets. The first one is the TCCON GGG2014 archive (Wunch et al. 2011).
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The second one is the HIPPO aircraft measurement archive (Wofsy et al. 2011). The
third one is the aircraft archive built by the FP6 GEOMON project that gathers 47
campaigns (see the list in Table 3). The fourth one is the CONTRAIL aircraft archive
(Machida et al. 2008, Matsueda et al. 2008, Sawa et al. 2008). The fifth one gathers
the regular aircraft measurements made at South Great Plain (SGP, OK, USA) between
November 2007 and December 2012 by Biraud et al. (2013). We compare the model to
each individual measurement, but distinguish between the statistics above 1500 m
above ground level (free troposphere, FT) and those below 1500 m (boundary layer,
BL). As a simple loose quality control, aircraft measurements for which the misfits are
larger than 10 ppm in absolute value are discarded.
Mission
Location
AASE-II
North Arctic, North America,
Eastern Pacific
ABLE_2B
Amazon, Brazil
ASHOE
Pacific
AIA
North East Tasmania,
Australia
BIBLE-A
Western Pacific
BIBLE-B
Western Pacific
BIBLE-C
Western Pacific
BIK
Bialystok, Poland
CAR
Eastern Colorado, USA
CARIBIC
Europe, Atlantic, Africa,
Middle-East
CERES
Les Landes, France
COBRA-2000
North America
COBRA-2003
North America
North America
COBRA-2004
CRYSTAL
Southern North America,
Caribbean
FTL
Northern Brazil
GRI
Scotland, GB
HAA
Hawaii, USA
HFM
North-East United States
HNG
Hungary
North America
INTEX-NA
LEF
Northern Central United
States
ORL
Orléans, France
MASTUEDA
North Australia to Japan
Period
Jan.- Mar.1992
April – May 1987
Feb. – Nov. 1994
Jun. 1991 - Sep. 2000
Sep 1998
Aug 1999
Nov 2000
Feb. 2002 – June 2007
Nov. 1992 - Dec. 2002
Nov. 1997 - Aug.
2001
May 2005 – June 2005
Jul.- Aug. 2000
May-June 2003
May-August 2004
May - Jul. 2002
Dec. 2000 - Jul. 2002
July 2001 – Sep. 2007
May. 1999 - Dec. 2002
Nov. 1999 - Nov. 2002
July-August 2004
Apr. 1998 - Dec. 2002
Apr. 1993 – March
2003
Validation report for the inverted CO2 fluxes, v15r2|
Organisation
Contact
NASA
B. Anderson
Harvard Univ.
S. Wofsy
NASA
S. Gaines, S.
Hipskind
CSIRO
P. Steele
NIES
T. Machida
NIES
T. Machida
NIES
T. Machida
LSCE
P. Ciais
NOAA
P. Tans, C.
Sweeney
MPI-C
C. A. M.
Brenninkmeijer
MPI-BGC
C. Gerbig
Harvard Univ.
S. Wofsy
Harvard Univ.
S. Wofsy
Harvard Univ.
S. Wofsy
Harvard Univ.
S. Wofsy
P. Tans, C.
Sweeney
NOAA
LSCE
NOAA
P. Ciais
P. Tans, C.
Sweeney
P. Tans, C.
Sweeney
LSCE
P. Ciais
NASA
NOAA
S. Vay
P. Tans, C.
Sweeney
LSCE
P. Ciais
MRI
H. Mastueda
NOAA
Copernicus Atmosphere Monitoring Service
Copernicus Atmosphere Monitoring Service
PEM-TROP-ADC8
PEM-TROP-AP3B
PEM-TROP-BDC8
PEM-TROP-BP3B
PEM-WEST-A
PEM-WEST-B
PFA
POLARIS
PRE-AVE
South Pacific Basin
US, Central America, NorthWest South America, East
Pacific
South Pacific Basin
South Pacific Basin
Western Pacific Basin, North
of Equator
Western and Eastern Pacific
Basin, North of Equator
Alaska, United States
North-West Pacific, Alaska
and the Arctic
North America
RTA
Rarotonga, South Pacific
SAN
Northern Brazil
SOLVE-DC8
Arctic
SONEX
North Atlantic
SPADE
North-West America
Western North America,
North-East Pacific
mid-Western USA.to North
Pacific
STRAT
SUCCESS
TOTE-VOTE
TRACE-A-DC8
TRACE-P-DC8
Mid-west USA
Arctic and Eastern-south
Pacific
North Pacific Basin
TRACE-P-P3B
North Pacific Basin
YAK
Siberia
BARCA-A
Amazon, Brazil
BARCA-B
Amazon, Brazil
Sept-Oct.1996
Aug-Sept.1996
Mar-April,1999
Mar-April 1999
Sept.-Oct., 1991
Feb.-Mar., 1994
Jun. 1999 - Dec. 2002
Apr.- Sep. 1997
January 2004
Apr. 2000 - Dec. 2002
Dec. 2000 - May 2002
Nov. 1999 - March
2000
Oct.- Nov. 1997
Nov. 1992 – Oct. 1993
May.- Dec. 1995/1996
April-May 1996
Dec.- Feb. 1995/1996
Sept.- Oct., 1992
Mar-Apr 2001
Mar-Apr 2001
Apr. – Sep. 2006
Nov. 2008
May 2009
NASA
S. Vay
NASA
B. Anderson
NASA
S. Vay
NASA
S. Vay
NASA
B. Anderson
NASA
NOAA
B. Anderson
P. Tans, C.
Sweeney
Harvard Univ.
S. Wofsy
Harvard Univ.
NOAA
S. Wofsy
P. Tans, C.
Sweeney
P. Tans, C.
Sweeney
NASA
S. Vay
NASA
B. Anderson
Harvard Univ.
S. Wofsy
NASA
S. Vay
NASA
B. Anderson
NASA
B. Anderson
NASA
S. Vay
NASA
LSCE
S. Vay
P. Ciais, J.-D.
Paris
Harvard Univ.
C. Gerbig
Harvard Univ.
C. Gerbig
NOAA
Table 3: Characteristics of the 45 aircraft campaigns from the FP6 GEOMON CO2 Airborne Data Archive, and of
the two BARCA campaigns that were not in the initial archive.
Figure 3 shows the distribution of the statistics of the CAMS inversions and that of the
corresponding Poor man’s simulation for each dataset: the five independent ones
(TCCON, HIPPO, CONT, GEOM, SGP, with FT and BL separated) and a sixth one made
of the assimilated measurements (SURFACE). The distribution is made of statistics for
each station (TCCON, SURFACE), for each airport (CONT), or for each flight campaign:
the minimum, the 25th, 50th and 75th percentiles are shown with usual boxes and
whiskers. As expected, the inversion systematically performs better than the Poor
man. The inversions usually fit their assimilated data, the column measurements and
the aircraft free troposphere measurements within 2 ppm (the median of the RMS is
usually about 1 ppm). The fits with aircraft profiles in the boundary layer are usually
better than 3 ppm. The time series of aircraft measurements and posterior simulation
for HIPPO and CONTRAIL flights are reproduced in Appendix B.
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Figure 3: Box and whisker plot showing the statistics of the misfits between the Poor man’s simulation and the
posterior CAMS simulation for each evaluation dataset.
The mean bias (standard deviation) of the posterior simulation in the free troposphere
is 0.1 (1.3), 0.1 (1.0), -0.2 (1.1) ppm for GEOMON, HIPPO and SGP, respectively.
Acknowledgements
The author is very grateful to the many people involved in the surface and aircraft CO2
measurements and in the archiving of these data that were kindly made available to him
by various means. TCCON data were obtained from the TCCON Data Archive, operated
by the California Institute of Technology from the website at http://tccon.ornl.gov/. Mass
fluxes for the LMDZ transport model have been provided by Y. Yin, R. Locatelli and P.
Bousquet. Some of this work was performed using HPC resources of DSM-CCRT and of
CCRT under the allocation t2016012201 made by GENCI (Grand Équipement National de
Calcul Intensif).
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Appendix A: Time series of the fit to the dependent surface
measurements
The mean departure (bd, model minus observations), the associated standard deviation
(σd), the mean assigned observation error standard deviation (σo) and the departure
RMS normalised by σo are also indicated for each station. These statistics appear in green
when RMS/σo ≤ 1 and in orange otherwise.
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Appendix B: Time series of the fit to the independent
measurements
The aircraft profiles are shown per day (in the form YYMMDD) and per flight, first HIPPO,
then CONTRAIL. The posterior model simulation and the measurements are shown in
green lines and red dots, respectively. The abscissa is both time (each dash corresponds
to a day of measurements) and mole fraction (the distance beween two dashes
corresponds to 10 ppm). The measurements are reported here on the 39 model levels
and not at their true height.
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