Validation report for the CO2 fluxes estimated by atmospheric inversion, v19r1 Version 1.0 - Copernicus ...
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ECMWF COPERNICUS REPORT
Copernicus Atmosphere Monitoring Service
Validation report for the CO 2 fluxes
estimated by atmospheric inversion, v19r1
Version 1.0
Issued by: CEA / Frédéric Chevallier
Date: 02/08/2020
REF.: CAMS73_2018SC2_D73.1.4.1-2019_v3_202008_Validation inversion CO2
fluxes_v1Copernicus 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.
Author 2 of 20Copernicus Atmosphere Monitoring Service
Contributors
CEA
Frédéric Chevallier
Author 3 of 20Copernicus Atmosphere Monitoring Service Table of Contents 1. Introduction 5 2. Inversion configuration 5 3. Evaluation 13 3.1 Fit to the assimilated measurements 13 3.2 Fit to unassimilated aircraft measurements 13 3.3 Fit to TCCON GGG2014 15 3.4 Country and annual scale CO2 budgets Error! Bookmark not defined. Acknowledgements 17 References 17 Author 4 of 20
Copernicus Atmosphere Monitoring Service 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 CAMS and its precursor projects GEMS/MACC/MACC-II/MACC-III (Chevallier 2020, and references therein). Here, we synthesize the evaluation of version 19r1 that was released in August 2020. Version 19r1 covers the period between January 1979 and December 2019. It mainly improves compared to the earlier v18r3 presented in Chevallier (2019) by a revision of the assimilated data1 and by the use of new prior information for the emissions from the use of fossil fuels and the production of cement. The presentation of the evaluation procedure is primarily based on the fit of the inversion posterior simulation to large databases: ObsPack Globalview+_v5.0 of Cooperative Global Atmospheric Data Integration Project (2019), ObsPack NRT v5.2 of NOAA Carbon Cycle Group ObsPack Team (2020), and the Total Carbon Column Observing Network (TCCON) GGG2014 archive (Wunch et al. 2011). In addition, time series at national annual scale are shown and briefly discussed. 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 LMDz6A (Remaud et al. 2018), that uses the deep convection model of Emanuel (1991). 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 (GCP- GridFED version 2019.1, Jones et al., 2020), (ii) monthly ocean fluxes (Denvil-Sommer et al. 2019 with updates described in Friedlingstein et al., 2019 2 ), 3-hourly (when available) or monthly biomass burning emissions (GFED 4.1s until the end of 20193 and GFAS afterwards) and climatological 3-hourly 1 Measurements after December 2019 are used to constrain the year 2019 better, but fluxes for those months are not publicly distributed. 2 This database covers the period 1985-2018. We use the monthly values for the years 1985 and 2018 before and after it, respectively. 3 Before 1997, a monthly climatology of this database is used. Author 5 of 20
Copernicus Atmosphere Monitoring Service
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 4.6.9.5. 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 3-hourly variations but their inter-annual
variations over land 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 initially represented from the variance of the high frequency variability of the de-seasonalized
and de-trended CO2 time series of the daily-mean measurements at a given location. The values are
then adjusted, first by inflating all error variances by the number of measurements at a given location
within each calendar day, then by averaging consecutive measurements and defining the resulting
error variance as the average of the individual error variances.
Version 19r1 analyzed 41.4 years of surface measurements, from January 1979 to May 2020. The
assimilated measurements are surface air-sample measurements of the CO2 dry air mole fraction
made at 147 sites over the globe. These data are a carefully-selected subset of five databases of
atmospheric measurements:
• the NOAA Earth System Research Laboratory Observation Package
(https://www.esrl.noaa.gov/gmd/ccgg/obspack/, Cooperative Global Atmospheric Data
Integration Project, 2019, and NOAA Carbon Cycle Group Obspack Team, 2020),
• the Réseau Atmosphérique de Mesure des Composés à Effet de Serre database (RAMCES,
http://www.lsce.ipsl.fr/),
• the Integrated Carbon Observation System- Atmospheric Thematic Center (ICOS-ATC,
https://icos-atc.lsce.ipsl.fr/),
• the World Data Centre for Greenhouse Gases archive (WDCGG, https://gaw.kishou.go.jp/),
• the Global Environmental Database (GED) maintained by the Center for Global
Environmental Research (CGER) of NIES (http://db.cger.nies.go.jp/portal/)
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The detailed list of selected 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 41.4 years, which is documented by the associated Bayesian error
statistics
Figure 1- Location of the assimilated measurements over the globe for each year in v19r1.
Table 1 - List of the continuous sites used in v19r1 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 provider. Note that only a subset of the data at each site is selected, based on local time, on the
quality flag and also excluding outliers.
Locality (indentifier) Period Source
Alert, Nunavut, CA (ALT) 1988-2019 NOAA/ EC
Alert, Nunavut, CA (ALT) 1988-2019 NOAA/ EC
Amsterdam Island, FR (AMS) 1981-2020 ICOS/ LSCE
Argyle, Maine, US (AMT) 2003-2020 NOAA/ ESRL
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Azovo, Siberia, RU (AZV) 2007-2017 NIES
Behchoko, Northwest Territories, CA (BCK) 2010-2019 NOAA/ EC
Baring Head, NZ (BHD) 1979-2018 NOAA/ NIWA
Bratt’s Lake Saskatchewan, CA (BRA) 2009-2019 NOAA/ EC
Berezorechka, Siberia, RU (BRZ) 2002-2017 NIES
Barrow, Alaska, US (BRW) 1979-2020 NOAA/ ESRL
Cambridge Bay, Nunavut Territory (CBY) 2012-2019 NOAA/ EC
Candle Lake, Saskatchewan, CA (CDL) 2002-2011 NOAA/ EC
Churchill, CA (CHL) 2011-2019 NOAA/ EC
Chibougamau, Quebec, CA (CHM) 2007-2011 NOAA/ EC
Monte Cimone, IT (CMN) 2018-2019 NOAA/ ICOS
Monte Cimone, IT (CMN) 1979-2019 WDCGG/ IAFMS
Cape Ochiishi, JP (COI) 1985-2002 WDCGG/ NIES
Chapais, Quebec, CA (CPS) 2011-2019 NOAA/ EC
Cape Point, SA (CPT) 1993-2018 NOAA/ SAWS
Carbon in Arctic Reservoirs Vulnerability
Experiment, US (CRV) 2011-2020 NOAA/ ESRL
Demyanskoe, Siberia, RU (DEM) 2005-2017 NIES
Egbert, Ontario, CA (EGB) 2005-2019 NOAA/ EC
Estevan Point, British Columbia, CA (ESP) 2009-2019 NOAA/ EC
Esther, Alberta, CA (EST) 2010-2019 NOAA/ EC
East Trout Lake, Saskatchewan, CA (ETL) 2005-2019 NOAA/ EC
Fraserdale, CA (FSD) 1990-2019 NOAA/ EC
Hateruma, JP (HAT) 1993-2002 WDCGG/ NIES
Hidden Peak (Snowbird), Utah, US (HDP) 2006-2015 NOAA/ NCAR
Harvard Forest, Massachusetts, US (HFM) 2012-2018 NOAA/ HU
Hohenpeissenberg, DE (HPB) 2015-2020 NOAA/ Scripps
Hegyhatsal tower, 115m level, HU (HUN0115) 1994-2018 NOAA/ HMS
Igrim, Siberia, RU (IGR) 2004-2013 NIES
Inuvik,Northwest Territories, CA (INU) 2012-2020 NOAA/ EC
Ivittuut, Greenland, DK (IVI) 2011-2014 ICOS/ LSCE
Tenerife, Canary Islands, ES (IZO) 1984-2018 NOAA/ AEMET
Jubany, AR (JBN) 1994-2009 WDCGG/ IAA
Jungfraujoch, CH (JFJ) 2004-2020 NOAA/ Univ. Of Bern
Jungfraujoch, CH (JFJ) 2016-2019 NOAA/ ICOS-ATC
Kasprowy Wierch, High Tatra, PL (KAS) 1996-2018 NOAA/ AGH
Hungary, HU (KPS) 1981-1999 WDCGG/ HMS
Karasevoe, Siberia, RU (KRS) 2004-2017 NIES
Park Falls, Wisconsin, US (LEF) 2000-2020 NOAA/ ESRL
Lac La Biche, Alberta, CA (LLB) 2000-2019 NOAA/ EC
Lamto, CI (LTO) 2008-2020 ICOS/ LSCE
Lutjewad, NL (LUT) 2006-2020 NOAA/RUG
Mace Head, County Galway, IE (MHD) 1992-2020 ICOS/ LSCE
Mt. Bachelor Observatory, US (MBO) 2012-2020 NOAA/ ESRL
Mauna Loa, Hawaii, US (MLO) 1979-2020 NOAA/ ESRL
Minamitorishima, JP (MNM) 1993-2018 NOAA/ JMA
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Noyabrsk, Siberia, RU (NOY) 2005-2017 NIES
Niwot Ridge, Colorado, US (NWR) 2005-2015 NOAA/ NCAR
Marys Peak, Oregon, US (OMP) 2007-2018 NOAA/ ESRL
Observatoire Pérenne de l'Environnement, FR
(OPE) 2011-2020 ICOS/ LSCE
Silverton, Oregon, US (OSI) 2012-2018 NOAA/ OSU
Walton, Oregon, US (OWA) 2012-2017 NOAA/OSU
Yaquina Head, Oregon, US (OYQ) 2007-2011 NOAA/ OSU
Pallas-Sammaltunturi, GAW Station, FI (PAL) 2000-2017 NOAA/ FMI
Pallas-Sammaltunturi, GAW Station, FI (PAL) 2017-2020 NOAA/ ICOS
Plateau Rosa, IT (PRS) 2000-2018 NOAA/ CESI RICERCA
Rio Branco, BR (RBA) 2007-2014 NOAA/ INPE
Ridge Hill, UK (RGL) 2012-2017 NOAA/ UNIVBRIS
Ryori, JP (RYO) 1987-2018 NOAA/ JMA
Southern Great Plains, Oklahoma, US (SGP) 2003-2018 NOAA/ LBNL-ARM
Tutuila, American Samoa (SMO) 1979-2020 NOAA/ ESRL
Hyytiala, FI (SMR) 2012-2020 NOAA/ Scripps
Sonnblick, AT (SNB) 1999-2018 WDCGG/ UBAA
Shenandoah National Park, US (SNP) 2008-2020 NOAA/ ESRL
Storm Peak Laboratory (SPL) 2005-2015 NOAA/ NCAR
South Pole, Antarctica, US (SPO) 1979-2020 NOAA/ ESRL
Schauinsland, Baden-Wuerttemberg, DE (SSL) 1979-2018 NOAA/ UBA
Syowa Station, Antarctica, JP (SYO) 1984-2018 NOAA/ Tohoku University
Vaganovo, Siberia, RU (SVV) 2006-2014 NIES
Tacolneston, UK (TAC) 2013-2017 NOAA/ ESRL
Turkey Point, Ontario (TPD) 2012-2019 NOAA/ EC
Vaganovo, Siberia, RU (VGN) 2008-2017 NIES
Weybourne, UK (WAO) 2007-2017 NOAA/ UEA
Moody, Texas, US (WKT) 2003-2018 NOAAA/ ESRL
Sable Island, Nova Scotia (WSA) 1992-2019 NOAA/ EC
Yakutsk, Siberia, RU (YAK) 2005-2013 NIES
Yonagunijima, JP (YON) 1997-2018 NOAA/ JMA
Ny-Alesund, Svalbard, Norway and Sweden
(ZEP) 2015-2017 NOAA/ NILU
Ny-Alesund, Svalbard, Norway and Sweden
(ZEP) 2017-2020 NOAA/ ICOS
Table 2 - Same as Table 1 but for the flask-sampling sites. Note that only a subset of the data at each site is
selected, avoiding outliers.
Locality (indentifier) Period Source
Alert, Nunavut, CA (ALT) 1985-2020 NOAA/ ESRL
Alert, Nunavut, CA (ALT) 1985-2018 NOAA/ Scripps
Alert, Nunavut, CA (ALT) 1991-2018 NOAA/ CSIRO
Alert, Nunavut, CA (ALT) 1988-2019 NOAA/ EC
Amsterdam Island, FR (AMS) 1982-1990 NOAA/ ESRL
Amsterdam Island, FR (AMS) 2001-2017 RAMCES/ LSCE
Anmyeon-do, KR (AMY) 2013-2019 NOAA/ ESRL
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Ascension Island, GB (ASC) 1979-2020 NOAA/ ESRL
Assekrem, DZ (ASK) 1995-2019 NOAA/ ESRL
St. Croix, Virgin Islands, USA (AVI) 1979-1990 NOAA/ ESRL
Terceira Island, Azores, PT (AZR) 1979-2020 NOAA/ ESRL
Baltic Sea, PL (BAL) 1992-2011 NOAA/ ESRL
Baja California Sur, MX (BCS) 1997-2009 NOAA/ Scripps
Bering Island, RU (BER) 1986-1994 WDCGG/ MGO
Baring Head, NZ (BHD) 1999-2019 NOAA/ ESRL
Baring Head, NZ (BHD) 1979-2018 NOAA/ Scripps
Baring Head, NZ (BHD) 1979-2018 NOAA/ NIWA
St. Davids Head, Bermuda, GB (BME) 1989-2010 NOAA/ ESRL
Tudor Hill, Bermuda, GB (BMW) 1989-2020 NOAA/ ESRL
Barrow, Alaska, US (BRW) 1979-2020 NOAA/ ESRL
Barrow, Alaska, US (BRW) 1979-2018 NOAA/ Scripps
Begur, ES (BGU) 2000-2017 RAMCES/ IC·3
Cold Bay, Alaska, US (CBA) 1979-2020 NOAA/ ESRL
Cold Bay, Alaska, US (CBA) 1995-2018 NOAA/ Scripps
Cape Ferguson, Queensland, AU (CFA) 1991/2018 NOAA/ CSIRO
Cape Grim, Tasmania, AU (CGO) 1984-2020 NOAA/ ESRL
Cape Grim, Tasmania, AU (CGO) 1991-2018 NOAA/ CSIRO
Cape Grim, Tasmania, AU (CGO) 1994-2018 NOAA/ Scripps
Christmas Island, Republic of Kiribati (CHR) 1984-2020 NOAA/ ESRL
Christmas Island, Republic of Kiribati (CHR) 1979-2018 NOAA/ Scripps
Centro de Investigacion de la Baja Atmosfera, ES
(CIB) 2009-2020 NOAA/ ESRL
Cape Meares, Oregon, US (CMO) 1982-1997 NOAA/ ESRL
Cape Point, SA (CPT) 2012-2020 NOAA/ ESRL
Crozet Island, FR (CRZ) 1991-2019 NOAA/ ESRL
Casey Station, AU (CYA) 1997-2018 NOAA/ CSIRO
Drake Passage (DRP) 2006-2020 NOAA/ ESRL
Dongsha Island, TW (DSI) 2010-2020 NOAA/ ESRL
Easter Island, CL (EIC) 1994-2019 NOAA/ ESRL
Estany Llong, ES (ELL) 2008-2015 NOAA/ Scripps
Estevan Point, British Columbia, CA (ESP) 1993-2002 NOAA/ CSIRO
Finokalia, Crete, GR (FIK) 1999-2017 RAMCES/ LSCE
Mariana Islands, Guam (GMI) 1979-2020 NOAA/ ESRL
Dwejra Point, Gozo, MT (GOZ) 1993-1999 NOAA/ ESRL
Gunn Point, AU (GPA) 2010-2015 NOAA/ CSIRO
Halley Station, Antarctica, GB (HBA) 1983-2020 NOAA/ ESRL
Hanle, IN (HLE) 2000-2016 RAMCES/ LSCE
Hohenpeissenberg, DE (HPB) 2006-2020 NOAA/ ESRL
Humboldt State University, US (HSU) 2008-2017 NOAA/ ESRL
Hegyhatsal, HU (HUN) 1993-2020 NOAA/ ESRL
Storhofdi, Vestmannaeyjar, IS (ICE) 1992-2020 NOAA/ ESRL
Ivittuut, Greenland, DK (IVI) 2007-2014 RAMCES/ LSCE
Tenerife, Canary Islands, ES (IZO) 1991-2020 NOAA/ ESRL
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Key Biscayne, Florida, US (KEY) 1979-2020 NOAA/ ESRL
Kotelnyj Island, RU (KOT) 1986-1993 WDCGG/ MGO
Cape Kumukahi, Hawaii, US (KUM) 1979-2020 NOAA/ ESRL
Cape Kumukahi, Hawaii, US (KUM) 1979-2018 NOAA/ Scripps
Sary Taukum, KZ (KZD) 1997-2009 NOAA/ ESRL
Plateau Assy, KZ (KZM) 1997-2009 NOAA/ ESRL
Lac La Biche, Alberta, CA (LLB) 2008-2013 NOAA/ ESRL
Lulin, TW (LLN) 2006-2020 NOAA/ ESRL
Lampedusa, IT (LMP) 2006-2020 NOAA/ ESRL
Ile grande, FR (LPO) 2004-2013 RAMCES/ LSCE
Mawson, AU (MAA) 1990-2018 NOAA/ CSIRO
Mould Bay, Nunavut, CA (MBC) 1980-1997 NOAA/ ESRL
High Altitude GCOC, Mexico (MEX) 2009-2020 NOAA/ ESRL
Mace Head, County Galway, IE (MHD) 1991-2020 NOAA/ ESRL
Mace Head, County Galway, IE (MHD) 1996-2017 RAMCES/ LSCE
Sand Island, Midway, US (MID) 1985-2020 NOAA/ ESRL
Mt. Kenya, KE (MKN) 2003-2011 NOAA/ ESRL
Mauna Loa, Hawaii, US (MLO) 1979-2020 NOAA/ ESRL
Mauna Loa, Hawaii, US (MLO) 1991-2018 NOAA/ CSIRO
Mauna Loa, Hawaii, US (MLO) 1979-2018 NOAA/ Scripps
Macquarie Island, AU (MQA) 1991-2018 NOAA/ CSIRO
Farol De Mae Luiza Lighthouse, BR (NAT) 2010-2020 NOAA/ ESRL
Farol De Mae Luiza Lighthouse, BR (NAT) 2010-2015 NOAA/ IPEN
Gobabeb, NA (NMB) 1997-2020 NOAA/ ESRL
Niwot Ridge, Colorado, US (NWR) 1979-2020 NOAA/ ESRL
Obninsk, RU (OBN) 2004-2009 NOAA/ESRL
Olympic Peninsula, WA, USA (OPW) 1984-1990 NOAA/ ESRL
Otway, Victoria, AU (OTA) 2005-2014 NOAA/ CSIRO
Ochsenkopf, DE (OXK) 2003-2020 NOAA/ ESRL
Pallas-Sammaltunturi, GAW Station, FI (PAL) 2002-2020 NOAA/ ESRL
Pic du Midi, FR (PDM) 2001-2015 RAMCES/ LSCE
Pacific Ocean (POC) 1987-2017 NOAA/ ESRL
Palmer Station, Antarctica, US (PSA) 1979-2019 NOAA/ ESRL
Palmer Station, Antarctica, US (PSA) 1996-2018 NOAA/ Scripps
Point Arena, California, US (PTA) 1999-2011 NOAA/ ESRL
Kermadec Island, Raoul Island, NZ (RK1) 1982-2018 NOAA/ Scripps
Ragged Point, BB (RPB) 1987-2020 NOAA/ ESRL
South China Sea (SCS) 1991-1998 NOAA/ ESRL
Shangdianzi, CN (SDZ) 2009-2015 NOAA/ ESRL
Mahe Island, SC (SEY) 1996-2020 NOAA/ ESRL
Southern Great Plains, Oklahoma, US (SGP) 2002-2020 NOAA/ ESRL
Shemya Island, Alaska, US (SHM) 1985-2020 NOAA/ ESRL
Ship between Ishigaki Island and Hateruma
Island, JP (SIH) 1993-2005 WDCGG/ Tokohu Univ.
Shetland, Scotland, GB (SIS) 1992-2003 NOAA/ CSIRO
Tutuila, American Samoa (SMO) 1981-2018 NOAA/ Scripps
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South Pole, Antarctica, US (SPO) 1979-2020 NOAA/ ESRL
South Pole, Antarctica, US (SPO) 1991-2018 NOAA/ CSIRO
South Pole, Antarctica, US (SPO) 1979-2018 NOAA/ Scripps
Ocean Station M, NO (STM) 1981-2009 NOAA/ ESRL
Summit, GL (SUM) 1997-2019 NOAA/ ESRL
Syowa Station, Antarctica, JP (SYO) 1986-2019 NOAA/ ESRL
Tacolneston, UK (TAC) 2014-2016 NOAA/ ESRL
Tae-ahn Peninsula, KR (TAP) 1990-2020 NOAA/ ESRL
Trinidad Head, California, US (THD) 2002-2017 NOAA/ ESRL
Hydrometeorological Observatory of Tiksi, RU
(TIK) 2011-2018 NOAA/ ESRL
Trainou 180m agl, FR (TR3) 2006-2017 RAMCES/ LSCE
Tromelin Island, F (TRM) 1998-2007 RAMCES/ LSCE
Tierra Del Fuego, Ushuaia, AR (USH) 1994-2019 NOAA/ ESRL
Wendover, Utah, US (UTA) 1993-2020 NOAA/ ESRL
Ulaan Uul, MN (UUM) 1992-2020 NOAA/ ESRL
Sede Boker, Negev Desert, IL (WIS) 1995-2020 NOAA/ ESRL
Mt. Waliguan, CN (WLG) 1990-2019 NOAA/ ESRL
Ny-Alesund, Svalbard, Norway and Sweden
(ZEP) 1994-2020 NOAA/ ESRL
Author 12 of 20Copernicus Atmosphere Monitoring Service 3. Evaluation We have run the LMDz global transport model using the surface fluxes and the initial CO2 state inferred by the inversion as boundary conditions and now compare it with dependent and independent observations. 3.1 Fit to the assimilated measurements Figure 2 shows the posterior root mean square (RMS) and bias of the model-minus-measurement difference as a function of the corresponding error statistics that we have assigned at each assimilated data. Measurement error is negligible here and the assigned error statistics refer to transport model errors and to representation errors (see Section 2). As expected, the inversion fits the assimilated data within the standard deviation of the assigned observation uncertainty. Biases are usually less than 1 ppm in absolute value. Figure 2 - Statistics of the differences between the posterior inversion simulation and individual assimilated surface measurements as a function of the assigned observation error standard deviation for each measurement site. The statistics cover the full assimilation period, starting in 1979 and including some of 2020. 3.2 Fit to unassimilated aircraft measurements Following the approach defined in Chevallier et al. (2019), we now focus on the continuous or flask dry air mole fraction measurements made by aircraft in the free troposphere. The free troposphere is simply defined here as the atmospheric layer between 2 and 7 km above sea level (asl). The measurements are all from ObsPack Globalview+ v5.0 and NRT v5.2 for the period 1979-2020. All model equivalents to individual data are publicly available from Author 13 of 20
Copernicus Atmosphere Monitoring Service http://dods.lsce.ipsl.fr/invsat/CAMS/v19r1_obspack5.txt and other may be made on request to copernicus-support@ecmwf.int. Figure 3 - Model-minus-observation differences and standard deviations per measurement program. The number of measurement per site, campaign or program varies between 7 (MRC) and 1,374,054 (CON). The programs are ranked by increasing mean latitude (North is on the right), irrespective of their latitudinal coverage (which is large of several tens of degrees for ORC, TOM and CON). These mean latitudes are shown in the middle of the panel. The statistics cover the period 1979–2020. The biases (Figure 3) are within 1 ppm and usually less than 0.5 ppm. There is no obvious latitudinal trend, and therefore no obvious flaw of the model vertical mixing (Stephens et al., 2007). Standard deviations vary with the fraction of land masses in a given latitude, as expected. They are about 2 ppm in the northern hemisphere and over the Amazon. When taking all free tropospheric aircraft data together, the posterior simulation deviates from the measurements by 0.0±1.6 ppm (bias ± standard deviation), which is within the specification (key performance indicator) of the CAMS CO2 inversion. Author 14 of 20
Copernicus Atmosphere Monitoring Service 3.3 Fit to TCCON GGG2014 Figure 4 shows the misfit statistics for the column retrievals at each TCCON station. For the comparison, the model has been convolved with the retrieval averaging kernels. All available TCCON station records are shown for the sake of completeness, but sites Pasadena and JPL are located in urban areas that are not well represented at the horizontal resolution of the transport model (3.75o in longitude and 1.875o in latitude): in this case the statistics logically show large negative model biases. Apart from these urban stations, absolute biases are less than 1 ppm at all sites. In non- urban sites, the standard deviation is usually about 1 ppm, but it reaches 2.2 ppm at the Zugspitze mountain site. We note that the model usually fits TCCON retrievals better than the satellite retrievals presented by Wunch et al. (2017). Figure 4 - Statistics of the difference between the posterior model and individual TCCON measurements, ordered by increasing latitude indices in the LMDz model (and without any specific order when 2 sites have the same latitude index in LMDz, like Paris and Orléans). A site may appear several times if several instruments have been used over time there. The statistics cover the period 2004-2019. 3.4 Country and annual scale CO2 budgets The aggregation of the inversion results at country scale is based on the country mask from http://themasites.pbl.nl/tridion/en/themasites/hyde/. The resulting annual CO2 budgets for v19r1 (Figure 5) are rather similar to those presented for v18r3 (Chevallier, 2019). We still note an increased uptake in 2011 for Australia, that is consistent, although smaller in amplitude, with other studies using different types of measurements (satellite XCO2 retrievals, satellite observations of vegetation activity, …) that reported an anomalous uptake in Australia during this particular La Niña episode (Poulter et al. 2013, Detmers et al. 2015, Ma et al. 2016). The impact of previous La Niña episodes can also be seen in the figure. Author 15 of 20
Copernicus Atmosphere Monitoring Service Figure 5 - Time series of the total flux (natural+anthropogenic) in v19r1 at annual scale (solid blue) and at 5-yearly scale with its 1-σ uncertainty (dashed blue with the light blue envelop) in large countries or in the European Union. Additionnally, emissions from land use, land use change and forestry (LULUCF) emission (green) and from the energy sector (grey) reported to UNFCCC in 2020 are displayed when available. Large countries are defined by surface areas larger than 3,000,000 km2. Positive values denote sources to the atmosphere (emissions), while negative values denote storage in soils and vegetation (sink). The model grid points associated to each country appear in red on the global maps. Author 16 of 20
Copernicus Atmosphere Monitoring Service 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/. Obspack data were obtained from https://www.esrl.noaa.gov/gmd/ccgg/obspack/. Mass fluxes for the LMDz transport model have been provided by M. Remaud. Some of this work was performed using HPC resources of DRF-CCRT and of CCRT under allocation A0090102201 made by GENCI (Grand Équipement National de Calcul Intensif). References 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., 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. Chevallier, F., Description of the CO2 inversion production chain. CAMS deliverable CAMS73_2018SC2_D73.5.2.1-2020_202004_CO2 inversion production chain_v1. http://atmosphere.copernicus.eu/, 2020. Chevallier, F., Validation report for the inverted CO2 fluxes, v18r3. CAMS deliverable CAMS73_2018SC2_ D73.1.4.1-2018-v2_201911. http://atmosphere.copernicus.eu/, 2019b. Chevallier, F., Remaud, M., O'Dell, C. W., Baker, D., Peylin, P., and Cozic, A.: Objective evaluation of surface- and satellite-driven CO2 atmospheric inversions, Atmos. Chem. Phys., accepted, 2019. Cooperative Global Atmospheric Data Integration Project. (2019). Multi-laboratory compilation of atmospheric carbon dioxide data for the period 1957-2018; obspack_co2_1_GLOBALVIEWplus_v5.0_2019_08_12 [Data set]. NOAA Earth System Research Laboratory, Global Monitoring Division. https://doi.org/10.25925/20190812 Denvil-Sommer, A., Gehlen, M., Vrac, M., and Mejia, C.: LSCE-FFNN-v1: a two-step neural network model for the reconstruction of surface ocean pCO2 over the global ocean, Geosci. Model Dev., 12, 2091–2105, https://doi.org/10.5194/gmd-12-2091-2019, 2019. Detmers, R. G., O. Hasekamp, I. Aben, S. Houweling, T. T. van Leeuwen, A. Butz, J. Landgraf, P. Köhler, L. Guanter, and B. Poulter, Anomalous carbon uptake in Australia as seen by GOSAT, Geophys. Res. Lett., 42, 8177–8184, doi:10.1002/2015GL065161, 2015. Author 17 of 20
Copernicus Atmosphere Monitoring Service 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
Copernicus Atmosphere Monitoring Service Stephens, B. B., et al.: Weak northern and strong tropical land carbon uptake from vertical profiles of atmospheric CO2, Science, 316, 1732–1735, 2007. 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. Wunch, D., Wennberg, P. O., Osterman, G., Fisher, B., Naylor, B., Roehl, C. M., O'Dell, C., Mandrake, L., Viatte, C., Kiel, M., Griffith, D. W. T., Deutscher, N. M., Velazco, V. A., Notholt, J., Warneke, T., Petri, C., De Maziere, M., Sha, M. K., Sussmann, R., Rettinger, M., Pollard, D., Robinson, J., Morino, I., Uchino, O., Hase, F., Blumenstock, T., Feist, D. G., Arnold, S. G., Strong, K., Mendonca, J., Kivi, R., Heikkinen, P., Iraci, L., Podolske, J., Hillyard, P. W., Kawakami, S., Dubey, M. K., Parker, H. A., Sepulveda, E., García, O. E., Te, Y., Jeseck, P., Gunson, M. R., Crisp, D., and Eldering, A.: Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) XCO2 measurements with TCCON, Atmos. Meas. Tech., 10, 2209-2238, https://doi.org/10.5194/amt-10-2209-2017, 2017. Author 19 of 20
Copernicus Atmosphere Monitoring Service ECMWF - Shinfield Park, Reading RG2 9AX, UK Contact: info@copernicus-atmosphere.eu atmosphere.copernicus.eu copernicus.eu ecmwf.int
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