Validation report for the inverted CO fluxes, v15r4 · South Pole, Antarctica, US (SPO) 1979-2016...

Validation report for the

inverted CO2 fluxes, v15r4

version 1.1

Issued by: CEA

Date: 26/01/2017

REF.: CAMS73_2015S2_ D73.1.4.2-1979-2015-v2_201701

Copernicus Atmosphere Monitoring Service


Validation report for the inverted CO2 fluxes, v15r4|

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, v15r4

version 1.1

CEA (Frédéric Chevallier)

Date: 26/01/2017

REF.: CAMS73_2015S2_D73.1.4.2-1979-

2015-v2_201701




Contents:

1 Introduction .................................................................................................... 2

2 Inversion configuration ..................................................................................... 2

3 Evaluation ....................................................................................................... 8

3.1 Benchmarking using a poor man’s inversion .................................................. 8

3.2 Fit to the assimilated measurements ............................................................ 9

3.3 Fit to the independent measurements ........................................................... 9

Appendix A: Time series of the fit to the dependent surface measurements ............. 12

Appendix B: Time series of the fit to the independent measurements ...................... 25




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 2016a, and references therein). Here, we synthesize the evaluation of version 15r4

that was released in November 20161. Version 15r2 improves compared to the earlier v15r2 shown in Chevallier (2016b) (i) by the assimilation of more data for year 2015,

(ii) by the extension of the assimilation window to the first six months of 2016 and to corresponding measurements, (iii) by an update of the 2015 global annual fossil fuel

estimate (using Le Quéré et al. 2016) and (iii) by a lesser weight given to observations when flasks and in-situ data co-exist on a same site. Overall, the quality of the data appears to be stable compared to v15r2, but we lack recent independent

measurements to visualise the better constraint given to the year 2015.

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, CDIAC and GCP), climatological monthly ocean fluxes, (Takahashi et al. 2009), monthly biomass burning emissions (GFED 4.1s

until 2014 and GFAS afterwards) 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

1 Previous version 15r2 has been evaluated by Chevallier (2016b) in a very similar format. Also

note that an intermediate version 15r3 has been used by Yue et al. (2017).




prior fluxes exhibit 3-hourly 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

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.




Figure 1: Location of the assimilated measurements over the globe for each year in v15r4.

Version 15r4 analysed more than 37 years of surface measurements, from January

1979 to June 2016 in a single data assimilation window. Note that only the data generated for the 37 full calendar years are delivered: the 6 months of 2016 only play

the role of a boundary condition to maximize the quality of the 2015 data. The assimilated measurements are surface air-sample measurements of the CO2 dry air

mole fraction made in 132 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.

Locality (indentifier) Period Source

Alert, Nunavut, CA (ALT) 1988-2016 WDCGG/ EC

Amsterdam Island, FR (AMS) 1981-2011 LSCE

Amsterdam Island, FR (AMS) 2012-2015 ICOS/ LSCE

Argyle, Maine, US (AMT) 2003-2016 NOAA/ ESRL

Anmyeon-do, KR (AMY) 1999-2014 WDCGG/ KMA

Barrow, Alaska, US (BRW) 1979-2016 NOAA/ ESRL

Candle Lake, CA (CDL) 2002-2012 WDCGG/ EC Centro de Investigacion de la Baja

Atmosfera, ES (CIB) 2009-2015 NOAA/ ESRL

Monte Cimone, IT (CMN) 1996-2015 WDCGG/ IAFMS

Cape Ochi-ishi, JP (COI) 1995-2002 WDCGG/ NIES




Cape Point, SA (CPT) 1993-2014 WDCGG/ SAWS

Egbert, CA (EGB) 2005-2016 WDCGG/ EC Estevan Point, British Columbia, CA

(ESP) 2009-2014 WDCGG/ EC

East Trout Lake, CA (ETL) 2005-2016 WDCGG/ EC

Frasedale, CA (FSD) 1990-2016 WDCGG/ EC

Hateruma, JP (HAT) 1993-2002 WDCGG/ NIES Hegyhatsal tower, 115m level, HU

(HUN0115) 1994-2015 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) 2004-2015 WDCGG/ Univ. Of Bern

K-puszta, HU (KPS) 1981-1999 WDCGG/ HMS

Park Falls, Wisconsin, US (LEF) 2000-2016 NOAA/ ESRL

Lac La Biche, Alberta, CA (LLB) 2007-2016 WDCGG/ EC

Mace Head, County Galway, IE (MHD) 1992-2009 LSCE

Mace Head, County Galway, IE (MHD) 2010-2015 ICOS/ LSCE

Mauna Loa, Hawaii, US (MLO) 1979-2016 NOAA/ ESRL

Minamitorishima, JP (MNM) 1993-2014 WDCGG/ JMA

Neuglobsow, DE (NGL) 1994-2013 WDCGG/ UBA Pallas-Sammaltunturi, GAW Station, FI

(PAL) 1999-2015 WDCGG/ FMI

Plateau Rosa, IT (PRS) 2000-2015 WDCGG/ CESI RICERCA

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) 1979-2016 NOAA/ ESRL

Sonnblick, AU (SNB) 1999-2015 WDCGG/ EEA

South Pole, Antarctica, US (SPO) 1979-2016 NOAA/ ESRL

Westerland, DE (WES) 1979-2013 WDCGG/ UBA

Moody, Texas, US (WKT) 2003-2016 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 v15r4 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.

Locality (indentifier) Period Source

Alert, Nunavut, CA (ALT) 1985-2016 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) 1979-2016 NOAA/ ESRL

Assekrem, DZ (ASK) 1995-2016 NOAA/ ESRL




St. Croix, Virgin Islands, USA (AVI) 1979-1990 NOAA/ ESRL

Terceira Island, Azores, PT (AZR) 1979-2016 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) 1999-2016 NOAA/ESRL

Baring Head, NZ (BHD) 1979-2015 WDCGG/ NIWA

St. Davids Head, Bermuda, GB (BME) 1989-2009 NOAA/ ESRL

Tudor Hill, Bermuda, GB (BMW) 1989-2016 NOAA/ ESRL

Barrow, Alaska, US (BRW) 1979-2016 NOAA/ ESRL

Cold Bay, Alaska, US (CBA) 1979-2016 NOAA/ ESRL

Cape Ferguson, AU (CFA) 1991-2014 WDCGG/ CSIRO

Cape Grim, Tasmania, AU (CGO) 1984-2016 NOAA/ ESRL

Churchill, CA (CHL) 2007-2014 WDCGG/ EC Christmas Island, Republic of Kiribati

(CHR) 1984-2016 NOAA/ ESRL

Cape Meares, Oregon, US (CMO) 1982-1998 NOAA/ ESRL

Crozet Island, FR (CRZ) 1991-2016 NOAA/ ESRL

Cape St. James, CA (CSJ) 1979-1992 WDCGG/ EC

Casey Station, AU (CYA) 1996-2013 WDCGG/ CSIRO Drake Passage (DRP) 2003-2016

NOAA/ ESRL

Easter Island, CL (EIC) 1994-2016 NOAA/ ESRL Estevan Point, British Columbia, CA

(ESP) 1992-2014 WDCGG/ EC Estevan Point, British Columbia, CA

(ESP) 1993-2001 WDCGG/ CSIRO

Finokalia, Crete, GR (FIK) 1999-2015 LSCE

Mariana Islands, Guam (GMI) 1979-2016 NOAA/ ESRL

Dwejra Point, Gozo, MT (GOZ) 1993-1998 NOAA/ ESRL

Halley Station, Antarctica, GB (HBA) 1983-2016 NOAA/ ESRL

Hanle, IN (HLE) 2000-2013 LSCE

Hohenpeissenberg, DE (HPB) 2006-2016 NOAA/ ESRL

Humboldt State University, US (HSU) 2008-2014 NOAA/ ESRL

Hegyhatsal, HU (HUN) 1993-2016 NOAA/ ESRL

Storhofdi, Vestmannaeyjar, IS (ICE) 1992-2015 NOAA/ ESRL

Grifton, North Carolina, US (ITN) 1992-1999 WDCGG/ ESRL

Ivittuut, Greenland, DK (IVI) 2007-2014 LSCE

Tenerife, Canary Islands, ES (IZO) 1991-2015 NOAA/ ESRL

Key Biscayne, Florida, US (KEY) 1979-2016 NOAA/ ESRL

Kotelny Island, RU (KOT) 1986-1993 WDCGG/ MGO

Cape Kumukahi, Hawaii, US (KUM) 1979-2016 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, TW (LLN) 2006-2014 NOAA/ ESRL

Lampedusa, IT (LMP) 2006-2016 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) 2009-2016 NOAA/ ESRL

Mace Head, County Galway, IE (MHD) 1991-2016 NOAA/ ESRL

Mace Head, County Galway, IE (MHD) 1996-2015 LSCE

Sand Island, Midway, US (MID) 1985-2016 NOAA/ ESRL

Mt. Kenya, KE (MKN) 2003-2011 NOAA/ ESRL

Mauna Loa, Hawaii, US (MLO) 1979-2016 NOAA/ ESRL

Macquarie Island, AU (MQA) 1990-2014 WDCGG/ CSIRO Farol De Mae Luiza Lighthouse, BR

(NAT) 2011-2016 NOAA/ ESRL

Gobabeb, NA (NMB) 1997-2016 NOAA/ ESRL

Niwot Ridge, Colorado, US (NWR) 1979-2015 NOAA/ ESRL

Olympic Peninsula, WA, USA (OPW) 1984-1990 NOAA/ ESRL

Ochsenkopf, DE (OXK) 2003-2016 NOAA/ ESRL Pallas-Sammaltunturi, GAW Station, FI

(PAL) 2001-2016 NOAA/ ESRL

Pic du Midi, FR (PDM) 2001-2015 LSCE

Pacific Ocean, 0N (POC000) 1987-2016 NOAA/ ESRL

Pacific Ocean, 5N (POCN05) 1987-2016 NOAA/ ESRL






Pacific Ocean, 5S (POCS05) 1987-2016 NOAA/ ESRL







Palmer Station, Antarctica, US (PSA) 1979-2016 NOAA/ ESRL

Point Arena, California, US (PTA) 1999-2011 NOAA/ ESRL

Puy de Dome, FR (PUY) 2001-2015 LSCE

Ragged Point, BB (RPB) 1987-2016 NOAA/ ESRL

South China Sea, 3N (SCSN03) 1991-1998 NOAA/ ESRL







Shangdianzi, CN (SDZ) 2009-2015 NOAA/ ESRL

Mahe Island, SC (SEY) 1980-2016 NOAA/ ESRL Southern Great Plains, Oklahoma, US

(SGP) 2002-2016 NOAA/ ESRL

Shemya Island, Alaska, US (SHM) 1985-2016 NOAA/ ESRL

Ship between Ishigaki Island and Hateruma Island, JP (SIH) 1993-2005

WDCGG/ Tohoku University




Shetland, Scotland, GB (SIS) 1992-2003 WDCGG/ CSIRO

Tutuila, American Samoa (SMO) 1979-2016 NOAA/ ESRL

South Pole, Antarctica, US (SPO) 1979-2016 NOAA/ ESRL

Ocean Station M, NO (STM) 1980-2009 NOAA/ ESRL

Summit, GL (SUM) 1997-2015 NOAA/ ESRL

Syowa Station, Antarctica, JP (SYO) 1986-2015 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) 1979-2015 WDCGG/ EC

Western Pacific Cruise (WPC) 2004-2013 NOAA/ ESRL Ny-Alesund, Svalbard, Norway and

Sweden (ZEP) 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

the prior ones. Over land, the poor man’s flux Fpm at location (x,y) and at time t is defined as:

Fpm (x,y,t) = Fprior (x,y,t) + k(year)·σ(x,y,t) (1)

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.

http://www.esrl.noaa.gov/gmd/ccgg/trends/




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). 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 Period Organisation Contact




AASE-II North Arctic, North America,

Eastern Pacific

Jan.- Mar.1992

NASA B. Anderson

ABLE_2B Amazon, Brazil

April – May 1987

Harvard Univ. S. Wofsy

ASHOE Pacific

Feb. – Nov. 1994

NASA S. Gaines, S.

Hipskind

AIA North East Tasmania,

Australia

Jun. 1991 - Sep. 2000

CSIRO P. Steele

BIBLE-A Western Pacific

Sep 1998

NIES T. Machida

BIBLE-B Western Pacific

Aug 1999

NIES T. Machida

BIBLE-C Western Pacific

Nov 2000

NIES T. Machida

BIK Bialystok, Poland

Feb. 2002 – June 2007

LSCE P. Ciais

CAR Eastern Colorado, USA

Nov. 1992 - Dec. 2002

NOAA P. Tans, C. Sweeney

CARIBIC Europe, Atlantic, Africa,

Middle-East

Nov. 1997 - Aug. 2001

MPI-C C. A. M.

Brenninkmeijer

CERES Les Landes, France

May 2005 – June 2005

MPI-BGC C. Gerbig

COBRA-2000 North America Jul.- Aug. 2000


COBRA-2003 North America May-June 2003


COBRA-2004 North America

May-August 2004


CRYSTAL Southern North America,

Caribbean May - Jul. 2002


FTL Northern Brazil Dec. 2000 - Jul. 2002


GRI Scotland, GB July 2001 – Sep. 2007

LSCE P. Ciais

HAA Hawaii, USA May. 1999 - Dec. 2002


HFM North-East United States Nov. 1999 - Nov. 2002


HNG Hungary

LSCE P. Ciais

INTEX-NA North America

July-August 2004

NASA S. Vay

LEF Northern Central United

States Apr. 1998 - Dec. 2002


ORL Orléans, France

LSCE P. Ciais

MASTUEDA North Australia to Japan Apr. 1993 – March

2003 MRI H. Mastueda PEM-TROP-A-

DC8 South Pacific Basin Sept-Oct.1996

NASA S. Vay

PEM-TROP-A-P3B

US, Central America, North-West South America, East

Pacific

Aug-Sept.1996

NASA B. Anderson PEM-TROP-B-

DC8 South Pacific Basin Mar-April,1999

NASA S. Vay PEM-TROP-B-

P3B South Pacific Basin Mar-April 1999

NASA S. Vay

PEM-WEST-A Western Pacific Basin, North

of Equator Sept.-Oct., 1991

NASA B. Anderson

PEM-WEST-B Western and Eastern Pacific

Basin, North of Equator Feb.-Mar., 1994

NASA B. Anderson

PFA Alaska, United States Jun. 1999 - Dec. 2002


POLARIS North-West Pacific, Alaska

and the Arctic Apr.- Sep. 1997





PRE-AVE North America

January 2004


RTA Rarotonga, South Pacific Apr. 2000 - Dec. 2002


SAN Northern Brazil Dec. 2000 - May 2002


SOLVE-DC8 Arctic Nov. 1999 - March

2000 NASA S. Vay

SONEX North Atlantic Oct.- Nov. 1997

NASA B. Anderson

SPADE North-West America Nov. 1992 – Oct. 1993

STRAT Western North America,

North-East Pacific May.- Dec. 1995/1996


SUCCESS mid-Western USA.to North

Pacific April-May 1996

NASA S. Vay

TOTE-VOTE Mid-west USA Dec.- Feb. 1995/1996

NASA B. Anderson

TRACE-A-DC8

Arctic and Eastern-south Pacific

Sept.- Oct., 1992

NASA B. Anderson

TRACE-P-DC8 North Pacific Basin

Mar-Apr 2001

NASA S. Vay

TRACE-P-P3B North Pacific Basin Mar-Apr 2001

NASA S. Vay

YAK Siberia Apr. – Sep. 2006

LSCE P. Ciais, J.-D.

Paris

BARCA-A Amazon, Brazil Nov. 2008

Harvard Univ. C. Gerbig

BARCA-B Amazon, Brazil May 2009

Harvard Univ. C. Gerbig

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.




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 0.2 (1.3), 0.2 (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).

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.

http://tccon.ornl.gov/




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.




References

Biraud, S. C., Torn, M. S., Smith, J. R., Sweeney, C., Riley, W. J., and Tans, P. P.: A multi-year record of airborne CO2 observations in the US Southern Great Plains, Atmos. Meas. Tech., 6, 751-763, doi:10.5194/amt-6-751-2013, 2013. Chevallier, F. (2016a), Description of the CO2 inversion production chain. CAMS deliverable CAMS73_2015S1_ D73.1.3_201603. http://atmosphere.copernicus.eu/

Chevallier, F. (2016b), Validation report for the inverted CO2 fluxes, v15r2. CAMS deliverable CAMS73_2015S1_ D73.1.2_201606. http://atmosphere.copernicus.eu/ Chevallier, F., N. Viovy, M. Reichstein, and P. Ciais: On the assignment of prior errors in Bayesian inversions of CO2 surface fluxes. Geophys. Res. Lett., 33, L13802, doi:10.1029/2006GL026496, 2006.

Chevallier, F., R. J. Engelen, C. Carouge, T. J. Conway, P. Peylin, C. Pickett-Heaps, M. Ramonet, P. J. Rayner, and I. Xueref-Remy (2009), AIRS-based versus flask-based estimation of carbon surface fluxes, J. Geophys. Res., 114, D20303, doi:10.1029/2009JD012311. Chevallier, F., T. Wang, P. Ciais, F. Maignan, M. Bocquet, A. Arain, A. Cescatti, J.-Q. Chen, H. Dolman, B. E. Law, H. A. Margolis, L. Montagni, and E. J. Moors: What eddy-covariance flux measurements tell us

about prior errors in CO2-flux inversion schemes. Global Biogeochem. Cy., 26, GB1021, doi:10.1029/2010GB003974, 2012. Emanuel, K.: A Scheme for Representing Cumulus Convection in Large-Scale Models, J. Atmos. Sci., 48, 2313–2329, doi:10.1175/1520-0469(1991)048<2313:ASFRCC2.0.CO;2, 1991. Krinner, G., Viovy, N., de Noblet-Ducoudré, N., Ogée, J., Polcher, J., Friedlingstein, P., Ciais,

P., Sitch, S., and Prentice, I. C.: A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochem. Cy., 19, GB1015, doi:10.1029/2003GB002199, 2005.

Le Quéré, C., Andrew, R. M., Canadell, J. G., Sitch, S., Korsbakken, J. I., Peters, G. P., Manning, A. C., Boden, T. A., Tans, P. P., Houghton, R. A., Keeling, R. F., Alin, S., Andrews, O. D., Anthoni, P., Barbero, L., Bopp, L., Chevallier, F., Chini, L. P., Ciais, P., Currie, K., Delire, C., Doney, S. C., Friedlingstein, P.,

Gkritzalis, T., Harris, I., Hauck, J., Haverd, V., Hoppema, M., Klein Goldewijk, K., Jain, A. K., Kato, E., Körtzinger, A., Landschützer, P., Lefèvre, N., Lenton, A., Lienert, S., Lombardozzi, D., Melton, J. R., Metzl, N., Millero, F., Monteiro, P. M. S., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S.-I., O'Brien, K., Olsen, A., Omar, A. M., Ono, T., Pierrot, D., Poulter, B., Rödenbeck, C., Salisbury, J., Schuster, U., Schwinger, J., Séférian, R., Skjelvan, I., Stocker, B. D., Sutton, A. J., Takahashi, T., Tian, H., Tilbrook, B., van der Laan-Luijkx, I. T., van der Werf, G. R., Viovy, N., Walker, A. P., Wiltshire, A. J., and Zaehle, S.: Global Carbon Budget 2016, Earth Syst. Sci. Data, 8, 605-649, doi:10.5194/essd-8-605-2016, 2016.

Locatelli, R., Bousquet, P., Hourdin, F., Saunois, M., Cozic, A., Couvreux, F., Grandpeix, J.-Y., Lefebvre, M.-P., Rio, C., Bergamaschi, P., Chambers, S. D., Karstens, U., Kazan, V., van der Laan, S., Meijer, H. A. J., Moncrieff, J., Ramonet, M., Scheeren, H. A., Schlosser, C., Schmidt, M., Vermeulen, A., and Williams, A. G.: Atmospheric transport and chemistry of trace gases in LMDz5B: evaluation and implications for

inverse modelling, Geosci. Model Dev., 8, 129-150, doi:10.5194/gmd-8-129-2015, 2015.

Machida, T., H. Matsueda, Y. Sawa, et al.: Worldwide measurements of atmospheric CO2 and other trace gas species using commercial airlines. J. Atmos. Oceanic. Technol. 25 (10), 1744-1754. doi: 10.1175/2008JTECHA1082.1, 2008. Matsueda, H., T. Machida, Y. Sawa, Y. Nakagawa, K. Hirotani, H. Ikeda, N. Kondo, and K. Goto: Evaluation of atmospheric CO2 measurements from new flask air sampling of JAL airliner observations, Pap.

Meteorolo. Geophys., 59, 1-17, 2008. Prather, M. J., C. D. Holmes, and J. Hsu: Reactive greenhouse gas scenarios: Systematic exploration of uncertainties and the role of atmospheric chemistry, Geophys. Res. Lett., 39, L09803, doi:10.1029/2012GL051440, 2012

http://atmosphere.copernicus.eu/




Sawa, Y., T. Machida, and H. Matsueda: Seasonal variations of CO2 near the tropopause observed by

commercial aircraft, J. Geophys. Res., 113, D23301, doi:10.1029/2008JD010568, 2008. Takahashi, T., S. C. Sutherland, R. Wanninkhof, C. Sweeney, R. A. Feely, D. W. Chipman, B. Hales, G. Friederich, F. Chavez, A. Watson, D. C. E. Bakker, U. Schuster, N. Metzl, H. Yoshikawa-Inoue, M. Ishii, T. Midorikawa, Y. Nojiri, C. Sabine, J. Olafsson, Th. S. Arnarson, B. Tilbrook, T. Johannessen, A. Olsen, Richard Bellerby, A. Körtzinger, T. Steinhoff, M. Hoppema, H. J. W. de Baar, C. S. Wong, Bruno Delille and N. R. Bates: Climatological mean and decadal changes in surface ocean pCO2, and net sea-air CO2 flux

over the global oceans. Deep-Sea Res. II, 56, 554-577, 2009. Wofsy SC, the HIPPO team and cooperating modellers and satellite teams: HIAPER Pole-to-Pole Observations (HIPPO): Fine grained, global scale measurements for determining rates for transport, surface emissions, and removal of climatically important atmospheric gases and aerosols, Phil. Trans. of the Royal Society A, 369(1943), 2073-2086, 2011.

Wunch, D., Toon, G. C., Blavier, J.-F. L., Washenfelder, R. A., Notholt, J., Connor, B. J., Griffith, D. W. T., Sherlock, V., and Wennberg, P. O., The Total Carbon Column Observing Network, Phil. Trans. R. Soc. A:2011369 2087-2112, doi10.1098/rsta.2010.0240, 2011. Yue, C., P. Ciais, A. Bastos, F. Chevallier, Y. Yin, and C. Rödenbeck, 2017: Abrupt seasonal transitions in land carbon uptake in 2015, Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2016-1167, in review.

Validation report for the inverted CO fluxes, v15r4 · South Pole, Antarctica, US (SPO) 1979-2016...

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