documentation of n o flux service

ECMWF COPERNICUS REPORT

Copernicus Atmosphere Monitoring Service

Documentation of N2O flux service

Description of the N2O inversion production chain

Issued by: NILU/Rona Thompson

Date: 22/06/2021

Ref: CAMS73_2018SC2_D5.2.3-2021_202106_Documentation N20 flux_v1

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.


CAMS73_2018SC2 -Documentation of N2O flux service of 20

Contributors

NILU Rona Thompson



Table of Contents

1. Atmospheric observations 7

1.1 Atmospheric measurements and locations 71.2 Processing of observations 7

2. Prior fluxes 8

3. Atmospheric transport model and input 8

4. Uncertainty estimates 9

4.1 Uncertainty in the observation space 94.2 Uncertainty in the state space 9

5. Inversion Methodology 9

6. Flux and concentration output 10

7. References 10


atmosphere.copernicus.eu copernicus.eu ecmwf.int

ECMWF - Shinfield Park, Reading RG2 9AX, UK Contact: [email protected]



Overview The CAMS N2O fluxes are estimated using the atmospheric inversion framework, PyVAR-N2O. Atmospheric inversions use observations of atmospheric mixing ratios, in this case, of N2O, and provide the fluxes that best explain the observations while at the same time being guided by a prior estimate of the fluxes. In other words, the fluxes are optimized to fit the observations within the limits of the prior and observation uncertainties. To produce the optimized (a posteriori) fluxes a number of steps are involved: first, the observations are pre-processed (described in section 1), second, a prior flux estimate is prepared (described in section 2), third mixing ratios are simulated using the prior fluxes and are used to estimate the model representation error (described in sections 3 and 4), and fourth, the inversion is performed (described in section 5).



1. Atmospheric observations

1.1 Atmospheric measurements and locations In total 123 ground-based sites, ship transects and aircraft profiles are included in the inversion (see Table 1 and Fig. 1). The term “site” refers to locations where there is a long-term record of observations from ground-based measurements, both from discrete samples (or “flasks”) and quasi-continuous sampling by in-situ instruments. N2O concentrations are measured using Gas Chromatographs equipped with an Electron Capture Detector (GC-ECD)

Some ground-based sites have measurements made by different laboratories, which provides a means to check for consistency between them. In addition to the sites, data from aircraft profiles and ship transects, i.e. the NOAA ESRL programme, are included. Up to now, satellite observations of N2O have been neither accurate nor precise enough to be used for estimating fluxes, with bias errors of ~7 ppb (parts-per billion) and precisions of ~1% (~3 ppb) (Xiong et al. 2014), compared to the 0.3 ppb achieved by ground-based observations. Very recently, a new product has become available for testing from the Infrared Atmospheric Sounding Interferometer (IASI) aboard the MetOp-A satellite. Tests with this satellite retrieval have been performed as part of the service evolution in 2020. Observations of N2O with sufficient accuracy for inverse modelling are available from the mid-1990s, thus the period covered by the inversion is from 1995 to 2019. The data density over time is shown in Fig. 2.

1.2 Processing of observations Owing to the small signal to noise ratio of N2O observations, it is critical to pre-process the observations to remove outliers and to correct for calibration differences between laboratories. Outliers are determined as observations outside 2-σ standard deviations of the running mean calculated over time window of 90 days, for flask observations, 0.5 days for continuous and aircraft observations, and 60 days for ship observations. The removal of outliers is performed iteratively until no more data are removed. Using this method, in the order of 2% of all observations are classified as outliers.

Calibration differences are determined relative to the NOAA-2006A scale maintained by NOAA ESRL GMD (Hall et al. 2007). A number of other laboratories/networks have their own scale, namely AGAGE who uses the SIO-1998 scale, NIES who use the NIES-94 scale, and Tohoku University who use the Tohoku scale. Even different laboratories operating on the same scale have differences between measurements. For this reason, the calibration differences with respect to NOAA-2006A are determined and specified by a regression coefficient and bias. These are found by either comparing the measurements made by a given laboratory with those of NOAA at the same location, where these both exist, or for laboratories with no sites co-located with a NOAA site, by inter-comparison of gas standards by the different laboratories. A summary of the calibration differences is given in Fig. 3. Using the regression coefficient and bias for each laboratory the observations are corrected to the NOAA-2006A scale. Previous analyses (Thompson et al. 2014), found a drift in the NOAA-2006A with respect to the SIO-1998 scale. Since then, AGAGE has revised their calibration scale to correct for drift in the gas references. All observations were transferred to this revised scale (SIO-16) in 2017 and this revision was included in the inversion. As a result, the correlation coefficient for the AGAGE network compared to the NOAA-2006A scale is much closer to one than previously (see Fig. 3).



Data from ground-based in-situ sites are generally assimilated into the inversion as daily afternoon averages (between 12:00 and 17:00), however, for mountain sites, a night-time average (between 00:00 and 06:00) is used to avoid times with complex circulation patterns, such as upslope winds, which cannot be reproduced with the current resolution of global atmospheric transport models. Aircraft and ship data are assimilated as the average of all observations falling in each grid-cell and time step of the transport model.

2. Prior fluxes A prior estimate of the total N2O flux with monthly resolution and inter-annually varying fluxes is prepared from a number of models and inventories (see Table 2). For the natural soil fluxes (specifically from unmanaged soils) an estimate from the land surface model OCN-v1.2-GCP2019 is used, which is driven by observation-based climate data, N-fertilizer statistics and modelled N-deposition (Zaehle et al. 2011). For the ocean fluxes, an estimate from the ocean biogeochemistry model PlankTOM-v10.2 is used, which is a prognostic model (Buitenhuis et al. 2018). In this model, the global ocean source is 2.6 TgN y-1 and is lower than the prior estimates used in previous inversions, of 5 to 6 TgN y-1 but is still in the range of ocean biogeochemistry models used in the GCP N2O Budget, of 2.3 to 4.5 TgN y-1. The change in ocean source value is due to the change in model, from the diagnostic to the prognostic version of the PlankTOM model. For biomass burning fluxes, the GFED-v4.1s data is used, which is based on fire activity data from the MODIS satellite and emission factors from Akagi et al. (2011). Lastly, for anthropogenic emissions (agriculture, industry, waste and fuel combustion), the EDGAR inventory data are used. EDGAR-v5 was used for the period 2005-2019, with 2016-2019 using the 2015 estimates as v5 covers only 2005-2015. For the period 1995-2004, EDGAR-v4.32 was used. All flux data are interpolated/averaged from their original resolution to that of the atmospheric transport model, i.e., 3.75° × 1.875° (longitude by latitude). The change in the ocean source means that the prior global total source is lower by about 3 TgN y-1 than in previous inversions.

3. Atmospheric transport model and input Atmospheric transport is modelled using an offline version of the Laboratoire de Meteorologie Dynamique model, LMDz5, which computes the evolution of atmospheric compounds using archived fields of winds, convection mass fluxes and planetary boundary layer (PBL) exchange coefficients that have been calculated using the online version nudged to ECMWF ERA interim winds. LMDz5 uses a Eulerian grid of 3.75° × 1.875° (longitude by latitude) and 39 hybrid pressure levels. Stratospheric losses of N2O through reaction with O(1D) and photolysis are calculated for each time-step and grid-cell using pre-calculated fields of O(1D) and photolysis rate from the online LMDz5 model.

Initial conditions, in this case, 3D fields of N2O concentration, are taken from forward simulations of the online model (run for at least 5 years) and are scaled to be consistent with observed concentrations at background sites.



4. Uncertainty estimates

4.1 Uncertainty in the observation space Uncertainty in the observation space is calculated as the quadratic sum of the measurement and transport uncertainties. The measurement uncertainty is assumed to be 0.3 ppb (approximately 0.1%) based on the recommendations of data providers. The transport uncertainty includes estimates of uncertainties in advective transport (based on the method of Rödenbeck et al. (2003)) and from a lack of subgrid-scale variability (based on the method of Bergamaschi et al. (2010)). The calculated total model transport uncertainty varied typically between 0.1 and 1.0 ppb, depending on the synoptic situation and the location, and had a mean of 0.2 ppb. We included an additional uncertainty on observations from southern mid to high latitudes of 1.0 ppb to account for known errors in stratosphere-troposphere exchange in the Southern Hemisphere in LMDz5. It is assumed that there are no cross-correlations between observations (i.e. the observation error covariance matrix is diagonal), which is a reasonable approximation considering that the observations are assimilated as afternoon (or night-time) means for ground-based data and at the grid-cell and time-step of the model for aircraft and ship data.

4.2 Uncertainty in the state space For the error in each land grid cell, the maximum magnitude of the flux in the cell of interest and its 8 neighbours is used, while for ocean grid cells the magnitude of the cell of interest only is used. This is done to allow the more degrees of freedom to change the fine spatial patterns of the fluxes on land, whereas on in the ocean, this method is not used to avoid having too large uncertainties in grid cells close to coastlines. The covariance was calculated as an exponential decay with distance and time using correlation scale lengths of 500 km over land and 1000 km over ocean and 3 months. The prior error covariance matrix is scaled so that the sum of its elements was equal to a global uncertainty of 2 TgN y-1, which is chosen to reflect an approximate uncertainty of about 12% in the total source.

5. Inversion Methodology PyVAR-N2O uses the Bayesian inversion method to find the optimal fluxes of N2O given prior information about the fluxes and their uncertainty, and observations of atmospheric N2O mole fractions. The method is the same as that used in Thompson et al. (2014) and the reader is referred to this paper for full details about the method. In summary, the optimal fluxes are those that minimize the following cost function (for derivation of the cost function see Rodgers et al. (2000)):

(1)

where the flux uncertainties are described by the error covariance matrix B, the observation uncertainties are described by the error covariance matrix R and H is a non-linear operator for atmospheric transport and chemistry (in Eq. 1, the matrix transpose is indicated by T). We use the variational approach to solve Eq. 1, which is an iterative process where the gradient of J is calculated

J(x) = 12(x − xb )

TB−1(x − xb )+12(H (x)− y)TR−1(H (x)− y)



at each iteration using a conjugate gradient algorithm (Lanczos 1950). This involves using an adjoint of the chemistry transport model (CTM) (Chevallier et al. 2005).

Posterior flux uncertainties are calculated from a Monte Carlo ensemble of inversions, based on the method of Chevallier et al. (2005). In each ensemble member, the prior fluxes were randomly perturbed to introduce errors consistent with those described by the prior error covariance matrix, B. The standard deviation of the posterior fluxes were assumed to be consistent with the probability distribution of the true fluxes.

6. Flux and concentration output The optimized N2O fluxes are saved as NetCDF files, where each file contains fluxes for one year at monthly temporal, and 3.75° × 1.875° (longitude by latitude) spatial, resolution. In addition, 3D N2O concentration fields, generated using the optimized fluxes, are saved. These are also NetCDF files with one file per month containing the N2O concentration every 3 hours for the 39 vertical levels and 3.75° × 1.875° (longitude by latitude) horizontal resolution.

7. References Akagi, S. K., Yokelson, R. J., Wiedinmyer, C., Alvarado, M. J., Reid, J. S., Karl, T., Crounse, J. D. and

Wennberg, P. O.: Emission factors for open and domestic biomass burning for use in atmospheric models, Atmos. Chem. Phys., 11(9), 4039-4072, doi:10.5194/acp-11-4039-2011, 2011.

Bergamaschi, P., Krol, M., Meirink, J. F., Dentener, F., Segers, A., van Aardenne, J., Monni, S., Vermeulen, A. T., Schmidt, M., Ramonet, M., Yver, C., Meinhardt, F., Nisbet, E. G., Fisher, R. E., O'Doherty, S. and Dlugokencky, E. J.: Inverse modeling of European CH4 emissions 2001-2006, J. Geophys. Res, 115(D22), D22309, doi:10.1029/2010jd014180, 2010.

Buitenhuis, E. T., Suntharalingam, P., & Le Quéré, C.: Constraints on global oceanic emissions of N2O from observations and models. Biogeosciences, 15(7), 2161–2175, doi:10.5194/bg-15-2161-2018, 2018

Chevallier, F., Fisher, M., Peylin, P., Serrar, S., Bousquet, P., Bréon, F. M., Chédin, A. and Ciais, P.: Inferring CO2 sources and sinks from satellite observations: Method and application to TOVS data, J. Geophys. Res., 110(D24309), doi:10.1029/2005jd006390, 2005.

Dutreuil, S., Bopp, L. and Tagliabue, A.: Impact of enhanced vertical mixing on marine biogeochemistry: lessons for geo-engineering and natural variability, Biogeosciences, 6(5), 901-912, doi:10.5194/bg-6-901-2009, 2009.

Hall, B. D., Sutton, G. S. and Elkins, J. W.: The NOAA nitrous oxide standard scale for atmospheric observations, J. Geophys. Res., 112(D09305), doi:10.1029/2006JD007954, 2007.

Machida, T., Matsueda, H., Sawa, Y., Nakagawa, Y., Hirotani, K., Kondo, N., Goto, K., Nakazawa, T., Ishikawa, K. and Ogawa, T.: Worldwide measurements of Atmospheric CO2 and Other Trace Gas Species Using Commercial Airlines, J. Atmos. Ocean. Tech, 25, 1744-1754, doi:10.1175/2008JTECHA1082.1, 2008.



Rödenbeck, C., Houweling, S., Gloor, M. and Heimann, M.: CO2 flux history 1982-2001 inferred from atmospheric data using a global inversion of atmospheric transport, Atmos. Chem. Phys, 3, 1919-1964, 2003.

Thompson, R. L., Chevallier, F., Crotwell, A. M., Dutton, G., Langenfelds, R. L., Prinn, R. G., Weiss, R. F., Tohjima, Y., Nakazawa, T., Krummel, P. B., Steele, L. P., Fraser, P., Ishijima, K. and Aoki, S.: Nitrous oxide emissions 1999 - 2009 from a global atmospheric inversion, Atmos. Chem. Phys., 14, 1801-1817, doi: 10.5194/acp-14-1801-2014, 2014.

Xiong, X., Maddy, E. S., Barnet, C., Gambacorta, A., Patra, P. K., Sun, F. and Goldberg, M.: Retrieval of nitrous oxide from Atmospheric Infrared Sounder: Characterization and validation, J. Geophys. Res., 119(14), 9107-9122, doi:10.1002/2013JD021406, 2014.

Zaehle, S., Ciais, P., Friend, A. D. and Prieur, V.: Carbon benefits of anthropogenic reactive nitrogen offset by nitrous oxide emissions, Nature Geosci, 4(9), 601-605, 2011.



Table 1. List of sites and campaigns. The symbol “V” means various locations. ID Network Latitude Longitude Altitude Type Description ABP NOAA -12.76 -38.16 6 FM Arembepe, Brazil AAO NOAA 40.10 -88.55 V AM Airborne Aerosol Observatory, USA ACG NOAA V V V AM Alaska Coast Guard, USA ALT NOAA 82.45 -62.52 205 FM Alert, Nunavut, Canada ALT CSIRO 82.45 -62.52 210 FM Alert, Nunavut, Canada ALT EC 82.45 -62.52 205 FM Alert, Nunavut, Canada AMT NOAA 45.01 -68.66 157 FM Amsterdam Island, France AMY NOAA 36.54 126.33 112 FM Anmyeon-do, Korea ASC NOAA -7.97 -14.4 90 FM Ascension Island, UK ASK NOAA 23.18 5.42 1847 FM Asseskrem, Algeria AZR NOAA 38.77 -27.38 24 FM Terceira Island, Azores, Portugal BAL NOAA 55.43 16.95 28 FM Baltic Sea, Poland BHD NOAA -41.41 174.87 90 FM Baring Head, New Zealand BKT NOAA -0.2 100.32 850 FM Bukit Kototabang, Indonesia BME NOAA 32.37 -64.65 17 FM St Davids Head, Bermuda, UK BMW NOAA 32.26 -64.88 60 FM Tudor Hill, Bermuda, UK BNE NOAA 40.80 -97.18 V AM Beaver Crossing, Nebraska, USA BRW NOAA 71.32 -156.61 13 FM Barrow, Alaska, USA BSC NOAA 44.18 28.66 5 FM Black Sea, Constanta, Romania CAR NOAA 40.37 -104.30 V AM Briggsdale, Colorado, USA CBA NOAA 55.21 -162.72 25 FM Cold Bay, Alaska, USA CFA CSIRO -19.28 147.05 2 FM Cape Ferguson, Australia CGO AGAGE -40.68 144.68 94 CM Cape Grim, Tasmania, Australia CGO NOAA -40.68 144.68 164 FM Cape Grim, Tasmania, Australia CGO CSIRO -40.68 144.68 94 FM Cape Grim, Tasmania, Australia CHL EC 58.75 -94.07 35 FM Churchill, Canada CHR NOAA 1.7 -157.15 2 FM Christmas Island, Republic of Kiribati CIB NOAA 41.81 -4.93 850 FM CIBA, Spain CMA NOAA 38.83 -74.32 V AM Cape May, New Jersey, USA CMN URB 44.18 10.70 2165 CM Monte Cimone, Italy COI NIES 43.16 145.5 45 CM Cape Ochi-ishi, Japan CPT NOAA -34.35 18.49 260 FM Cape Point, South Africa CRI CSIRO 15.08 73.83 60 FM Cape Rama, India CRV NOAA V V V AM CARVE aircraft campaigns CRZ NOAA -46.43 51.85 202 FM Crozet Island, France CYA CSIRO -66.28 110.53 55 FM Casey Station, Australia DND NOAA 47.50 -99.24 V AM Dahlen, North Carolina, USA DRP NOAA V V V SM Drake Passage Cruises DSI NOAA 20.7 116.73 8 FM Dongsha Island, Taiwan



EIC NOAA -27.15 -109.45 55 FM Easter Island, Chile ESP NOAA 49.38 -126.55 V AM Estevan Point, Canada ESP EC 49.38 -126.55 47 FM Estevan Point, Canada ESP CSIRO 49.38 -126.55 47 FM Estevan Point, Canada ETL NOAA 54.35 -104.98 V AM East Trout Lake, Saskatchewan, Canada GMI NOAA 13.39 144.66 6 FM Mariana Islands, Guam GPA CSIRO -12.25 131.05 25 FM Gunn Point, Australia HAA NOAA 21.23 -158.95 V AM Molokai Island, Hawaii, USA HAT NIES 24.06 123.81 11 CM Hateruma, Japan HBA NOAA -75.61 -26.21 35 FM Halley Station, Antarctica HFM NOAA 42.54 -72.17 V AM Harvard Forest, Massachusetts, USA HIL NOAA 40.07 -87.91 V AM Homer, Illinois, USA HPB NOAA 47.8 11.02 990 FM Hohenpeissenberg, Germany HSU NOAA 41.05 -124.73 8 FM Humboldt University, USA HUN NOAA 46.95 16.65 344 FM Hegyhatsal, Hungary ICE NOAA 63.25 -20.15 120 FM Storhofdi, Vestmannaeyjar, Iceland INX NOAA 39.72 -85.95 V AM INFLUX, Indianapolis, USA IZO NOAA 28.3 -16.48 2378 FM Izana, Tenerife, Canary Islands, Spain IZO AEM 28.30 -16.48 2397 CM Izana, Tenerife, Canary Islands, Spain JFJ EMPA 46.55 7.99 3580 CM JungfrauJoch, Switzerland KEY NOAA 25.67 -80.2 6 FM Key Biscayne, Florida, USA KUM NOAA 19.52 -154.82 8 FM Cape Kumukahi, Hawaii, USA KZD NOAA 44.08 76.87 600 FM Sary Taukum, Kazakhstan KZM NOAA 43.25 77.88 2524 FM Plateau Assy, Kazakhstan LEF NOAA 45.95 -90.27 V AM Park Falls, Wisconsin, USA LEF NOAA 45.93 -90.27 868 FM Park Falls, Wisconsin, USA LLB NOAA 54.95 -112.45 546 FM Lac La Biche, Alberta, Canada LLN NOAA 23.46 120.86 2867 FM Lulin, Taiwan LMP NOAA 35.51 12.61 50 FM Lampedusa, Italy MAA CSIRO -67.62 62.87 32 FM Mawson, Australia MEX NOAA 18.98 -97.31 4469 FM High-Alt. Glob. Clim. Obs., Mexico MHD AGAGE 53.33 -9.9 26 CM Mace Head, County Galway, Ireland MHD NOAA 53.33 -9.9 26 FM Mace Head, County Galway, Ireland MID NOAA 28.22 -177.37 11 FM Sand Island, Midway, USA MKN NOAA -0.06 37.3 3649 FM Mt Kenya, Kenya MLO NOAA 19.53 -155.58 3402 FM Mauna Loa, Hawaii, USA MLO CSIRO 19.53 -155.58 3397 FM Mauna Loa, Hawaii, USA MQA CSIRO -54.48 158.97 12 FM Macquarie Island, Australia NAT NOAA -5.51 -35.26 20 FM Farol de Mae Luiza Lighthouse, Brazil NHA NOAA 42.95 -70.63 V AM Worcester, Massachusetts, USA NMB NOAA -23.58 15.03 461 FM Gobabeb, Namibia NWR NOAA 40.05 -105.58 3526 FM Niwot Ridge, Colorado, USA



OXK NOAA 50.03 11.81 1185 FM Ochsenkopf, Germany PAL NOAA 67.97 24.12 565 FM Pallas-Sammaltunturi, Finland PFA NOAA 65.07 -147.29 V AM Poker Flat, Alaska, USA POC NOAA V V V SM Pacific Ocean Moorings PSA NOAA -64.92 -64 15 FM Palmer Station, Antarctica PTA NOAA 38.95 -123.73 22 FM Point Arena, California, USA RGL UBRIS 52.0 -2.54 294 CM Ridgehill, UK RPB AGAGE 13.17 -59.43 45 CM Ragged Point, Barbados RPB NOAA 13.16 -59.43 20 FM Ragged Point, Barbados RTA NOAA -21.25 -159.83 V AM Rarotonga, Cook Islands SCA NOAA 32.77 -79.55 V AM Charleston, South Carolina, USA SDZ NOAA 40.65 117.12 298 FM Shangdianzi, China SEY NOAA -4.68 55.53 3 FM Mahe Island, Seychelles SGP NOAA 36.62 -97.48 V AM Southern Great Plains, Oklahoma, USA SGP NOAA 36.62 -97.48 374 FM Southern Great Plains, Oklahoma, USA SHM NOAA 52.72 174.1 28 FM Shemya Island, Alaska, USA SIS CSIRO 60.08 -1.25 30 FM Shetland Islands, UK SMO AGAGE -14.23 -170.57 77 CM Tutuila, America Samoa, USA SMO NOAA -14.25 -170.57 47 FM Tutuila, America Samoa, USA SSL UBA 47.92 7.92 1213 CM Schauinsland, Germany SPO NOAA -89.98 -24.8 2815 FM South Pole, Antarctica SPO CSIRO -89.98 -24.8 2810 FM South Pole, Antarctica STM NOAA 66 2 7 FM Ocean Station M, Norway SUM NOAA 72.58 -38.42 3215 FM Summit, Greenland SYO NOAA -69 39.58 11 FM Syowa Station, Antarctica TAC UBRIS 52.52 1.14 241 CM Tacolneston Tall Tower, UK TAC NOAA 52.52 1.14 241 FM Tacolneston Tall Tower, UK TAP NOAA 36.73 126.13 21 FM Tae-anh Peninsula, Republic of Korea TGC NOAA 27.73 -96.86 V AM Sinton, Texas, USA THD AGAGE 41.05 -124.15 107 CM Trinindad Head, California, USA THD NOAA 41.05 -124.15 V AM Trinindad Head, California, USA THD NOAA 41.05 -124.15 112 FM Trinindad Head, California, USA UTA NOAA 39.9 -113.72 1332 FM Wendover, Utah, USA UUM NOAA 44.45 111.1 1012 FM Ulaan Uul, Mongolia WBI NOAA 41.72 -91.35 V AM West Branch, Iowa, USA WIS NOAA 30.86 34.78 482 FM Negev Desert, Israel WKT NOAA 31.32 -97.33 708 FM Moody, Texas, USA WLG NOAA 36.27 100.92 3815 FM Mt Waliguan, China WPC NOAA V V V SM Western Pacific Cruises WSA EC 43.93 -60.02 5 FM Sable Island, Canada ZEP NOAA 78.91 11.89 489 FM Ny-Alesund, Svalbard, Norway ZSF UBA 47.42 10.98 2671 CM Zugspitze, Germany



Table 2. Overview of prior fluxes (totals are given for the year 2010).

Category Data source Resolution Total (TgN y-1) Natural soils OCN v1.2 1.0°×1.0° 5.86 Coastal and open ocean PlankTOMv10.2 1.0°×1.0° 2.61 Agriculture EDGARv4.32/v5 0.1°×0.1° 3.89 Other anthropogenic EDGARv4.32/v5 0.1°×0.1° 1.25 Biomass burning GFED-4.1s 0.25°×0.25° 0.51 Total 14.12



Figure 1: Map of the observation network

NOAACSIRONOAA AIRAGAGENIESEMPAAEMUBAURBUNIBRISECCCNOAA SHIPflaskinïsitu



Figure 2. Availability of ground-based data over time by site and laboratory (number of observation per month). Shown are the tower and flask sampling sites (top) and aircraft based data (bottom).

AAO NOAAACG NOAABGI NOAABNE NOAACAR NOAACMA NOAACRV NOAADND NOAAESP NOAAETL NOAAFWI NOAAHAA NOAAHFM NOAAHIL NOAAHIP NOAAINX NOAALEF NOAANHA NOAAOIL NOAAPFA NOAARTA NOAASCA NOAASGP NOAATGC NOAATHD NOAA

1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019

Year



ABP NOAAALT NOAAALT CSIROALT ECAMT NOAAAMY NOAAASC NOAAASK NOAAAZR NOAABAL NOAABHD NOAABKT NOAABME NOAABMW NOAABRW NOAABSC NOAACBA NOAACFA CSIROCGO AGAGECGO NOAACGO CSIROCHL ECCHR NOAACIB NOAACMN URBCOI NIESCPT NOAACRI CSIROCRZ NOAACYA CSIRODSI NOAAEIC NOAAESP CSIROESP ECGMI NOAAGPA CSIROHAT NIESHBA NOAAHPB NOAAHSU NOAAHUN NOAAICE NOAAIZO AEMIZO NOAAJFJ EMPAJFJ EMPAKEY NOAAKUM NOAAKZD NOAAKZM NOAALEF NOAALLB NOAALLN NOAALMP NOAAMAA CSIROMEX NOAAMHD AGAGEMHD NOAAMID NOAAMKN NOAAMLO NOAAMLO CSIROMQA CSIRONAT NOAANMB NOAANWR NOAAOXK NOAAPAL NOAAPSA NOAAPTA NOAARGL UBRRPB AGAGERPB NOAASDZ NOAASEY NOAASGP NOAASHM NOAASIS CSIROSMO AGAGESMO NOAASPO NOAASPO CSIROSSL UBASSL UBASTM NOAASUM NOAASYO NOAATAC NOAATAC UBRTAP NOAATHD AGAGETHD NOAAUTA NOAAUUM NOAAWIS NOAAWKT NOAAWLG NOAAWSA ECZEP NOAAZSF UBA

1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019

Year

<22 ï 45 ï 89 ï 30>30

AAO NOAAACG NOAABNE NOAACAR NOAACMA NOAACRV NOAADND NOAAESP NOAAETL NOAAHAA NOAAHFM NOAAHIL NOAAINX NOAALEF NOAANHA NOAAPFA NOAARTA NOAASCA NOAASGP NOAATGC NOAATHD NOAA

1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019

Year



Figure 3. Calibration comparison to the NOAA scale (NOAA-2006A). The regression coefficient is shown for the comparison of the other laboratories scale with that of NOAA. For some laboratories/networks there is more than one site (i.e. AGAGE and CSIRO) and the comparison for each site where a comparison is possible

is shown.

0.7

0.8

0.9

1.0

1.1

1.2

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AGACSIECCAEMNIEUBAEMPFMI

documentation of n o flux service

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