validation report for the inverted co fluxes, v16r2 · copernicus atmosphere monitoring service...

ECMWF COPERNICUS REPORT

Copernicus Atmosphere Monitoring Service

Validation report for the inverted CO2 fluxes, v16r2 Version 1.0

Issued by: CEA / Frédéric Chevallier

Date: 23/01/2018

REF.: CAMS73_2015SC3_D73.1.4.2-1979-2016-v2_201801_Validation inverted CO2

fluxes_v1


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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.


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Contributors

CEA Frédéric Chevallier


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Table of Contents

1. Introduction 5

2. Inversion configuration 6

3. Evaluation 12

3.1 Benchmarking using a poor man’s inversion 12

3.2 Fit to the assimilated measurements 12

3.3 Fit to the independent measurements 13

3.4 Country and annual scale CO2 budgets 15

Acknowledgements 17

Appendix A: Time series of the fit to the dependent surface measurements 18

Appendix B: Time series of the fit to the independent measurements 28

References 34


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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 2017a, and references therein). Here, we synthesize the evaluation of version 16r2 that was released in November 20171. Version 16r2 covers the years between 1979 and 2016. It improves compared to the earlier v16r1 shown in Chevallier (2017b) (i) by the assimilation of data for the first half of year 20172, and (ii) by a change in the prior biomass burning and fossil fuel surface fluxes. The evaluation database has been much extended, thanks to NOAA’s Obspack archive: the statistics for the previous releases are not strictly comparable, but overall the quality seems to be stable. Section 2 describes the PyVAR-CO2 configuration that was used and Section 3 presents the evaluation synthesis.

1 Previous version 16r2 has been evaluated by Chevallier (2016b) in a very similar format. 2 Measurements for 2017 are used to constrain the year 2016 better, but 2017 fluxes are not publicly distributed.


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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 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), (ii) monthly ocean fluxes (Landschützer et al. 20153), 3-hourly (when available) or monthly biomass burning emissions (GFED 4.1s until 20164 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 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.

3 This database covers the period 1982-2011. We use the monthly values for the years 1982 and 2011 before and after it, respectively. 4 Before 1997, a monthly climatology of this database is used.


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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.

Figure 1- Location of the assimilated measurements over the globe for each year in v16r2.

Version 16r2 analyzed 38.5 years of surface measurements, from January 1979 to June 2017 in a single data assimilation window. The assimilated measurements are surface air-sample measurements of the CO2 dry air mole fraction made in 111 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 38.5 years, which is documented by the associated Bayesian error statistics.


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Table 1 - List of the continuous sites used in v16r2 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) 1988-2016 NOAA/ EC

Amsterdam Island, FR (AMS) 1981-2010 LSCE

Amsterdam Island, FR (AMS) 2012-2016 ICOS/ LSCE

Argyle, Maine, US (AMT) 2003-2017 NOAA/ ESRL

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

Barrow, Alaska, US (BRW) 1979-2017 NOAA/ ESRL

Candle Lake, CA (CDL) 2002-2011 NOAA/ EC

Monte Cimone, IT (CMN) 1996-2016 WDCGG/ IAFMS

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

Cape Point, SA (CPT) 1993-2015 NOAA/ SAWS

Egbert, CA (EGB) 2005-2016 NOAA/ EC

Estevan Point, British Columbia, CA (ESP) 2009-2016 WDCGG/ EC

East Trout Lake, CA (ETL) 2005-2017 NOAA/ EC

Frasedale, CA (FSD) 1990-2017 NOAA/ EC

Hateruma, JP (HAT) 1993-2002 WDCGG/ NIES

Hegyhatsal tower, 115m level, HU (HUN0115) 1994-2016 NOAA/ HMS

Ivittuut, Greenland, DK (IVI) 2011-2014 ICOS/ LSCE

Tenerife, Canary Islands, ES (IZO) 1984-2017 NOAA/ AEMET

Jubany, Antartica, AR (JBN) 1994-2009 WDCGG/ ISAC IAA

Jungfraujoch, CH (JFJ) 2004-2015 NOAA/ Univ. Of Bern

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

Park Falls, Wisconsin, US (LEF) 2000-2017 NOAA/ ESRL

Lac La Biche, Alberta, CA (LLB) 2007-2017 NOAA/ EC

Lutjewad, NL (LUT) 2006-2016 NOAA/RUG

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

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

Mauna Loa, Hawaii, US (MLO) 1979-2017 NOAA/ ESRL

Minamitorishima, JP (MNM) 1993-2017 WDCGG/ JMA

Neuglobsow, DE (NGL) 1994-2013 WDCGG/ UBA

Pallas-Sammaltunturi, GAW Station, FI (PAL) 1999-2015 NOAA/ FMI

Plateau Rosa, IT (PRS) 2000-2016 WDCGG/ CESI RICERCA

Puy de Dome, FR (PUY) 2011-2016 ICOS/ LSCE

Ryori, JP (RYO) 1987-2015 NOAA/ JMA

Tutuila, American Samoa (SMO) 1979-2017 NOAA/ ESRL

Sonnblick, AU (SNB) 1999-2016 WDCGG/ EEA

South Pole, Antarctica, US (SPO) 1979-2017 NOAA/ ESRL

Weybourne, UK (WAO) 2007-2016 NOAA/ UEA

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

Moody, Texas, US (WKT) 2003-2017 NOAAA/ ESRL

Sable Island, CA (WSA) 1992-2017 NOAA/ EC

Yonagunijima, JP (YON) 1997-2015 NOAA/ JMA


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Table 2 - Same as Table 1 but for the flask-sampling sites.

Locality (indentifier) Period Source

Alert, Nunavut, CA (ALT) 1985-2017 NOAA/ ESRL

Alert, Nunavut, CA (ALT) 1985-2015 NOAA/ Scripps

Alert, Nunavut, CA (ALT) 1979-2015 NOAA/ EC

Alert, Nunavut, CA (ALT) 1991-2015 NOAA/ CSIRO

Amsterdam Island, FR (AMS) 1979-1990 NOAA/ ESRL

Amsterdam Island, FR (AMS) 2003-2016 LSCE

Ascension Island, GB (ASC) 1979-2017 NOAA/ ESRL

Assekrem, DZ (ASK) 1995-2017 NOAA/ ESRL

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

Terceira Island, Azores, PT (AZR) 1979-2017 NOAA/ ESRL

Baltic Sea, PL (BAL) 1992-2011 NOAA/ ESRL

Baja California Sur, MX (BCS) 1997-2008 NOAA/ Scripps

Bering Island, RU (BER) 1986-1994 WDCGG/ MGO

Begur, ES (BGU) 2000-2016 LSCE/ IC·3

Baring Head, NZ (BHD) 1999-2016 NOAA/ ESRL

Baring Head, NZ (BHD) 1979-2015 NOAA/ Scripps

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

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

Tudor Hill, Bermuda, GB (BMW) 1989-2017 NOAA/ ESRL

Barrow, Alaska, US (BRW) 1979-2017 NOAA/ ESRL

Cold Bay, Alaska, US (CBA) 1979-2017 NOAA/ ESRL

Cape Grim, Tasmania, AU (CGO) 1984-2017 NOAA/ ESRL

Churchill, CA (CHL) 2007-2016 WDCGG/ EC

Christmas Island, Republic of Kiribati (CHR) 1984-2016 NOAA/ ESRL

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

Cape Point, SA (CPT) 2010-2017 NOAA/ ESRL

Crozet Island, FR (CRZ) 1991-2017 NOAA/ ESRL

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

Casey Station, AU (CYA) 1997-2015 NOAA/ CSIRO

Drake Passage (DRP) 2006-2017 NOAA/ ESRL

Easter Island, CL (EIC) 1994-2017 NOAA/ ESRL

Estany Llong, ES (ELL) 2008-2015 NOAA/ Scripps

Estevan Point, British Columbia, CA (ESP) 1992-2016 WDCGG/ EC

Estevan Point, British Columbia, CA (ESP) 1993-2001 WDCGG/ CSIRO

Finokalia, Crete, GR (FIK) 1999-2016 LSCE

Mariana Islands, Guam (GMI) 1979-2017 NOAA/ ESRL

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

Gunn Point, AU (GPA) 2010-2015 NOAA/ CSIRO

Halley Station, Antarctica, GB (HBA) 1983-2017 NOAA/ ESRL


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Hanle, IN (HLE) 2000-2016 LSCE

Hohenpeissenberg, DE (HPB) 2006-2017 NOAA/ ESRL

Hegyhatsal, HU (HUN) 1993-2017 NOAA/ ESRL

Storhofdi, Vestmannaeyjar, IS (ICE) 1992-2017 NOAA/ ESRL

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

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

Tenerife, Canary Islands, ES (IZO) 1991-2017 NOAA/ ESRL

Key Biscayne, Florida, US (KEY) 1979-2017 NOAA/ ESRL

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

Cape Kumukahi, Hawaii, US (KUM) 1979-2017 NOAA/ ESRL

Sary Taukum, KZ (KZD) 1997-2009 NOAA/ ESRL

Plateau Assy, KZ (KZM) 1997-2009 NOAA/ ESRL

Lac La Biche, Alberta, CA (LLB) 2008-2013 NOAA/ ESRL

Lampedusa, IT (LMP) 2006-2017 NOAA/ ESRL

Ile grande, FR (LPO) 2004-2013 LSCE

Mawson, AU (MAA) 1990-2015 NOAA/ CSIRO

Mould Bay, Nunavut, CA (MBC) 1980-1997 NOAA/ ESRL

High Altitude GCOC, Mexico (MEX) 2009-2017 NOAA/ ESRL

Mace Head, County Galway, IE (MHD) 1991-2017 NOAA/ ESRL

Mace Head, County Galway, IE (MHD) 1996-2016 LSCE

Sand Island, Midway, US (MID) 1985-2017 NOAA/ ESRL

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

Mauna Loa, Hawaii, US (MLO) 1979-2017 NOAA/ ESRL

Macquarie Island, AU (MQA) 1990-2015 NOAA/ CSIRO

Farol De Mae Luiza Lighthouse, BR (NAT) 2011-2017 NOAA/ ESRL

Gobabeb, NA (NMB) 1997-2017 NOAA/ ESRL

Niwot Ridge, Colorado, US (NWR) 1979-2017 NOAA/ ESRL

Obninsk, RU (OBN) 2004-2009 NOAA/ESRL

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

Ochsenkopf, DE (OXK) 2003-2017 NOAA/ ESRL

Pallas-Sammaltunturi, GAW Station, FI (PAL) 2001-2017 NOAA/ ESRL

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

Pacific Ocean (POC) 1987-2017 NOAA/ ESRL

Palmer Station, Antarctica, US (PSA) 1979-2017 NOAA/ ESRL

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

Kermadec Island, NZ (RK1) 1982-2015 NOAA/ Scripps

Ragged Point, BB (RPB) 1987-2017 NOAA/ ESRL

South China Sea (SCS) 1991-1998 NOAA/ ESRL

Mahe Island, SC (SEY) 1980-2017 NOAA/ ESRL

Southern Great Plains, Oklahoma, US (SGP) 2002-2017 NOAA/ ESRL

Shemya Island, Alaska, US (SHM) 1985-2017 NOAA/ ESRL Ship between Ishigaki Island and Hateruma

Island, JP (SIH) 1993-2005 WDCGG/ Tohoku University

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

Tutuila, American Samoa (SMO) 2016 NOAA/ ESRL

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


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

Summit, GL (SUM) 1997-2017 NOAA/ ESRL

Syowa Station, Antarctica, JP (SYO) 1986-2017 NOAA/ ESRL

Trinidad Head, California, US (THD) 2002-2017 NOAA/ ESRL

Trainou 180m agl, FR (TR3) 2006-2016 LSCE

Tromelin Island, F (TRM) 1998-2007 LSCE

Tierra Del Fuego, Ushuaia, AR (USH) 1994-2017 NOAA/ ESRL

Wendover, Utah, US (UTA) 1993-2017 NOAA/ ESRL

Ulaan Uul, MN (UUM) 1992-2017 NOAA/ ESRL

Sede Boker, Negev Desert, IL (WIS) 1995-2017 NOAA/ ESRL

Mt. Waliguan, CN (WLG) 1990-2017 NOAA/ ESRL Ny-Alesund, Svalbard, Norway and Sweden

(ZEP) 1994-2017 NOAA/ ESRL


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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 simulated by the vegetation model without any spatial information from the atmospheric observations, nor any temporal information within any given year.

3.2 Fit to the assimilated measurements 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.

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


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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 normalized by the observation error standard deviation for the analysis period (1979–2016) at one of the assimilated measurement site.

3.3 Fit to the independent measurements Comparisons are also made with independent dry air mole fraction measurements. We use the TCCON GGG2014 archive (Wunch et al. 2011) and the aircraft measurements gathered in the Obspack database. In contrast to previous validation reports, we do not refer to individual campaigns, nor to past aircraft archives. We compare the model to each individual measurement, but for the aircraft data, we 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. 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 two independent ones (TCCON and aircraft data, with FT and BL separated) and a third one made of the assimilated measurements (SURFACE). The distribution is made of statistics for each station (TCCON, SURFACE), 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 1.5 ppm). The fits with aircraft profiles in the boundary layer are mostly better than 3 ppm. Some time series of aircraft measurements and posterior simulation for HIPPO and CONTRAIL flights are reproduced in Appendix B. The mean bias (standard deviation) of the posterior simulation in the free troposphere is -0.3 (1.3) ppm.


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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. Note that due to the filter on aircraft measurements (see the main text), the Poor man’s statistics slightly depend on the CAMS simulation ones.

Figure 4 - National-annual-scale time series of the total natural flux in v16r1 (green) with its 1-σ uncertainty (yellow), of the LULUCF emission reported to UNFCCC (blue) and of the energy sector emission reported to UNFCCC in 2017 (grey). 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.


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3.4 Country and annual scale CO2 budgets The Country and annual scale CO2 budgets (Figures 4, 5, 6 and 7) are rather similar to the previous version (shown in Chevallier, 2017b). 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). Figure 5 – Continued.


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Figure 6 – Continued.

Figure 7 – Continued.


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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 Y. Yin, R. Locatelli and P. Bousquet. Some of this work was performed using HPC resources of DSM-CCRT and of CCRT under allocation A0010102201 made by GENCI (Grand Équipement National de Calcul Intensif).

http://tccon.ornl.gov/

https://www.esrl.noaa.gov/gmd/ccgg/obspack/


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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. Only data before 1 January 2017 are shown even though some data has been assimilated for year 2017.


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Appendix B: Time series of the fit to independent measurements The aircraft profiles are shown per day (in the form YYMMDD) and per flight, for the period June 1991-March 2004. Validation results for later periods are availabe on request (there are 52 6-row panels available in total). The posterior model simulation and the measurements are shown in blue lines and red dots, respectively. The abscissa is both time (each dash corresponds to a day of measurements) and mole fraction (the distance between 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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Ma, X., A. Huete, J. Cleverly, D. Eamus, F. Chevallier, J. Joiner, B. Poulter, Y. Zhang, L. Guanter, W. Meyer, Z. Xie, G. Ponce-Campos, 2016: Drought rapidly disseminates the 2011 large CO2 uptake in semi-arid Australia. Scientific Reports, 6. doi: 10.1038/srep37747. 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. Poulter, B., D. Frank, P. Ciais, R. B. Myneni, N. Andela, J. Bi, G. Broquet, J. G. Canadell, F. Chevallier, Y. Y. Liu, S. W. Running, S. Sitch and G. R. van der Werf: Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature, doi:10.1038/nature13376, 2014 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 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. 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.


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