validation of the ch surface flux inversion - reanalysis ... ·...

ECMWF COPERNICUS REPORT

Copernicus Atmosphere Monitoring Service

Validation of the CH4 surface flux inversion - reanalysis 2000-2016

Issued by: TNO / Arjo Segers

Date: 22/12/2017

Ref: CAMS73_2015SC2_D73.2.4.4-2016_201712_validation_CH4_2000-2016_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_2015SC2_D73.2.4.4-2016_Validation_CH4_2000-2016_v1 of 36

Contributors

TNO Arjo Segers

SRON Sander Houweling



Table of Contents

1. Production streams 6

2. Optimized emissions 7

2.1 Timeseries of monthly emissions 7

2.2 Emission maps 10

2.3 Emissions by latitude and time 10

2.4 Timeseries of yearly emissions 11

3. Comparison with NOAA surface observations 15

3.1 NOAA surface network 15

3.2 Number of data values 16

3.3 Example time series 16

3.4 Timeseries of statistics 18

4. Comparison with GOSAT XCH4 columns 20

4.1 GOSAT XCH4 product 20

4.2 TM5-GOSAT bias correction 20

4.3 Number of observations 21

4.4 Maps comparing GOSAT columns with simulations 21

4.5 Comparison by latitude and time 22

5. Comparison with TCCON XCH4 25

5.1 TCCON total CH4 columns 25

5.2 TCCON network 25

5.3 Timeseries of statistics 26

5.4 Comparison by latitude and time 26

6. Comparison with aircraft observations 30

6.1 Aircraft observations 30

6.2 KPI values 30

7. Summary and outlook 33

8. References 35



Introduction This report describes the CH4 surface flux inversion for the reanalysis period 2000-2016. The production is described in detail in the Description of the CH4 Inversion Production Chain report available from the CAMS website (Segers & Houweling, 2017). Two datasets of inverted fluxes are available for this period, originating from two production streams:

Dataset “v16r1” is based on analysis of NOAA surface observations only. This dataset covers the entire period 2000-2016, where years 2000-2015 were released before as “v15r2”.

Dataset “v16r1s” is based on analysis of NOAA surface observations and in addition satellite observations from GOSAT. This dataset covers the period 2009-2016 for which GOSAT data is available. For the years 2009-2015 this data set replaces the coarse resolution release “v15r2s” which provided results on 6ox4o globally instead of 3ox2o.

The CH4 surface fluxes described in this report are available through the CAMS data server:

http://apps.ecmwf.int/datasets/data/cams-ghg-inversions/ In addition, also concentrations and total columns are provided. Version id, release id, and filenames are described in Table 1.

Table 1 - CH4 flux files on the CAMS data server as described by this report, as well as additional concentration and total column files.

Version release data files description

v16r1 (surface obs.) 2000-2016

3ox2o globally

r1 (2017-12)

z_cams_l_tno-sron_yyyymm_v16r1_fg_sfc_mm_ch4flux.nc z_cams_l_tno-sron_yyyymm_v16r1_fg_sfc_1d_ch4.nc z_cams_l_tno-sron_yyyymm_v16r1_fg_sfc_6h_xch4.nc

4D-var first guess (a priori)

z_cams_l_tno-sron_yyyymm_v16r1_an_sfc_ch4flux.nc z_cams_l_tno-sron_yyyymm_v16r1_an_sfc_1d_ch4.nc z_cams_l_tno-sron_yyyymm_v16r1_an_sfc_6h_xch4.nc

4D-var analysis (a posteriori)

v16r1s (surface + GOSAT) 2009-2016

3ox2o globally

r1s (2017-12)

z_cams_l_tno-sron_yyyymm_v16r1s_fg_sfc_ch4flux.nc z_cams_l_tno-sron_yyyymm_v16r1s_fg_sfc_1d_ch4.nc z_cams_l_tno-sron_yyyymm_v16r1s_fg_sfc_6h_xch4.nc

4D-var first guess (a priori)

z_cams_l_tno-sron_yyyymm_v16r1s_an_sfc_ch4flux.nc z_cams_l_tno-sron_yyyymm_v16r1s_an_sfc_1d_ch4.nc z_cams_l_tno-sron_yyyymm_v16r1s_an_sfc_6h_xch4.nc

4D-var analysis (a posteriori)

http://apps.ecmwf.int/datasets/data/cams-ghg-inversions/



1. Production streams This report describes inversions from two production streams.

The “S1” stream reanalysis optimized surface fluxes of CH4 by assimilation of NOAA surface observations. The reanalysis period covers the years 2000-2016. Inversions were performed for target intervals of 3 years for 2000-2014, and 1 year for 2015 and 2016. Each target interval was extended with 6-month spin-up and spin-down periods.

The “S2” stream reanalysis optimized surface fluxes of CH4 by assimilation of NOAA surface observations (similar as “S1”) and in addition GOSAT XCH4 columns. This reanalysis covers the period 2009-2016 for which GOSAT data is available. Also here inversions were performed for 3-year intervals for 2009-2014 and 1 year for 2015 and 2016, extended with 6-month spin-up/spin-down periods.

Where possible, validation results are shown for the two streams together.



2. Optimized emissions The inversion system adjusts the CH4 emissions to bring simulated concentrations in better agreement with observations. This chapter summarizes the inverted emissions and shows the differences between a priori and a posteriori emissions.

2.1 Timeseries of monthly emissions As a summary of the adjustments, Figure 1 shows time series of monthly global emissions before and after inversion. The a posteriori emissions for the “S2” stream (NOAA+GOSAT) show in general similar timeseries as the “S1” stream (NOAA-only), but differences are visible too. For the wetlands category, the prior emissions reach their maximum for the northern-hemisphere summer. The optimized emissions show the same annual pattern, but with a significant higher peak (up to 25% more emissions), which is also reached 1-2 months later. For the NOAA+GOSAT stream, the height of the new peak is smaller than for the NOAA-only stream, but still larger than the a priori. Similar increments are seen for the rice emissions, which also show a higher annual maximum when optimized. Here the NOAA+GOSAT results are very similar to the NOAA-only results. The prior biomass burning emissions show strong inter-annual variations, except for 2012-2016 when for each year the 2012 inventory is used. This is a limitation of the use of the GFED v3.1 biomass burning emission inventory, which was chosen here for consistency with the preceding MACC inversions. For future inversions the CAMS GFAS emissions (2003 onwards) or GFED v4.1 (1997 onwards) are considered to replace the current inventory. Although the a priori biomass burning emissions might not be entirely correct for recent years, the a posteriori emissions are hardly changed by the optimization however. The largest changes (absolute as well as relative) are seen for the anthropogenic (‘other’) emissions. The prior anthropogenic emissions are stronger during the (northern hemisphere) winter than the summer. After inversion, the emissions during the winter are often lower than during the summer. This is especially visible for the NOAA-only stream, but also the NOAA+GOSAT stream shows this for most years (but less strong). Exception are the NOAA+GOSAT inversions for 2015 and 2016, which show instead increased “other” emissions during the northern hemisphere summer. A clear explanation for this could not be found yet. The use of 1-yearly inversion windows for the two most recent years might be an origin, but since this was also used for the NOAA-only stream it cannot be the only explanation. With the summer values after inversion being not too different from the a priori estimate, the inverted time series of the global anthropogenic emissions shows a shift in the seasonal pattern. Evaluation of the spatial patterns in the following paragraphs shows that the origin of the lower winter emissions is to be found mainly in Asia. For the total emissions (all categories together), the seasonal pattern of higher (northern hemisphere) summer emissions remains, since the wetland, rice, and fire emissions all reach their maximum then. The seasonal cycle of the emissions has been amplified by the inversion, with lower values in spring due to lower anthropogenic emissions, and higher summer values due to increased



wetland and rice emissions. The differences between NOAA+GOSAT and NOAA-only estimates for total emissions are less pronounced as seen for the individual categories.



Figure 1 - Time series of monthly global emissions for the optimized categories and the total. Expressed in units of Tg/day to avoid small scale fluctuations due to different month lengths.



2.2 Emission maps For an overview of the spatial changes, Figure 2 shows maps of the analyzed emissions as well as the analysis increments. The left and middle columns show the analyzed emission and analysis increment for the “S1” stream (NOAA-only) for the entire 2000-2016 period, while the column on the right shows the increment for the “S2” stream (NOAA+GOSAT) for 2009-2016. The increments are therefore not fully comparable, but since the inversions showed rather consistent results during their time ranges the maps still give a good indication of the differences. The rows show results for individual emission categories and the total. As seen already in the time series of total emissions, wetland emissions receive a net increase from the inversions. These emissions are increased in the main source regions of South-America and Africa. For the NOAA-only inversion also wetland emissions over Bangladesh are increased, while a net decrease is obtained from the NOAA+GOSAT inversion for this region. The latter coincides with lower wetland emissions over Indonesia which are also decreased by the NOAA+GOSAT inversion rather strongly (compared to the NOAA-only stream, where this decreases is much smaller). This suggests that the NOAA network in this region is too sparse to be able to constrain the wetland emissions here, or at least not able to distinguish the emissions from this source from the other sources present in the region. Rice emissions are increased for both inversions, but less pronounced for the NOAA+GOSAT stream. Changes in biomass burning emissions are small, but in general the inversions show a decrease. Anthropogenic (‘other’) emissions are strongly decreased over Asia, especially in China but also over India. Increases of anthropogenic emissions are seen over North and South America. The changes imposed by the NOAA+GOSAT inversion show more spatial variation than the inversion with NOAA-only, for example in Central Asia where a strong emission increase for this category is inverted. The total changes in emissions are dominated by the anthropogenic sources, and therefore show similar patterns with lower emissions in Asia and higher emissions in the Americas.

2.3 Emissions by latitude and time Figure 3 shows emissions as function of latitude and month. The left column shows the a posteriori emissions after the NOAA-only inversions for each of the four emission categories and the total. Middle column shows the corresponding inversion increments, while the right column shows the increments for the NOAA+GOSAT inversion (for the shorter time range 2009-2016). The annual cycles in the a posteriori emissions and the emission changes are very persistent. The wetland emissions show strong oscillating changes, with at northern latitudes lower zonal total emissions earlier in the year and higher emissions later on. Tropical wetland emissions are strongly increased by the NOAA+GOSAT inversion for the first months of 2015 and 2016; in the maps of Figure 2 this is visible as lower wetland emissions over Indonesia. Also the changes in rice emissions show an oscillating pattern, with increased emissions in (northern hemisphere) summer, and decreased emissions later in autumn. The NOAA+GOSAT inversion also introduces adjustments of rice emissions at equatorial latitudes, although these are small compared to the emission changes in Asia.



Biomass-burning emissions are hardly changed on average. Including GOSAT data in the observations leads to different changes in biomass burning emissions, with lower values around the equator while the NOAA-only inversion introduced higher values here. Largest emissions and emission changes are seen for the anthropogenic (‘other’) sources. Both inversion streams decrease these emissions around 30oN throughout all months. However, for the NOAA-only inversion the decrease becomes stronger for later years, while for the NOAA+GOSAT stream the decrease becomes less strong. Around the equator both inversions increase the emissions. At northern hemisphere mid-latitudes the ‘other’ emissions are decreased during winter and increased during summer. The total increments are dominated by the changes in anthropogenic emissions, but also the increased northern hemisphere summer emissions from wetlands and rice fields are visible.

2.4 Timeseries of yearly emissions For better insight in emission trends, Figure 4 shows timeseries of yearly emissions. Both global total emissions as well as zonal emissions per latitude band of 30o degrees are shown. The figures also include a posteriori emission numbers from TM5-4D-var runs performed by the JRC as part of the ESA CCI project (Chevallier et al., 2017); data provided by (P. Bergamaschi, personal communication). The configuration of the CAMS runs (described in this report) is based on the configuration used by the JRC for the MACC projects, but differs from the latest JRC runs in for example the combination of coarse and high resolution inversions and the use of archived convective fields from ERA-Interim. The global emission time series show an increasing trend. For the a priori emissions there is no increase from 2012 onwards because it uses the 2012 inventories; the a posteriori emissions are still increasing however. The a posteriori emissions show more variability than the a priori. For example, the CAMS NOAA-only inversions estimated higher emissions than the a priori during the first and latest years of the inversion period, but lower emissions in between. For some years a strong decrease is seen, for example around 2005, 2009, and 2013. The CAMS estimate using NOAA surface observations only is lower than the JRC estimate for the same period (2010-2015). The plots with emissions per zonal band show that this is mainly due to lower estimates for the tropical regions (30oS-30oN). In general there is a latitudinal difference between the CAMS and JRC inversions, with the CAMS inversions estimating higher emissions at northern latitudes than the JRC inversions; this is also seen in the figure with emissions for 2012 for all zonal bands. The inversions including GOSAT data are not too different from the surface-only inversions. The CAMS and JRC inversions including GOSAT are actually more similar than the inversions without satellite data. A similar variation over the years is present, with clearly higher emissions in 2014.



Figure 2 - Maps of average posteriori emissions for NOAA-only inversion over 2000-2016 period (left), and change in emissions with respect to a priori estimate (middle). Right column shows change in emissions for

NOAA+GOSAT inversion over 2009-2016 period.



Figure 3 - Zonal total emissions per month after NOAA-only inversion (left) and corresponding analysis increments (middle), both for 2000-2016 period; right column shows analysis increments for NOAA+GOSAT

inversion for 2009-2016.



Figure 4- Timeseries of yearly emissions from inversions described in this report (CAMS), as well as comparable inversions performed by JRC. Top panel: global total. Middle panels: emissions per 30o latitude

bands. Bottom panel shows emissions as function of latitude band for single year (2012).



3. Comparison with NOAA surface observations The analyzed surface observations for both streams consist of CH4 concentrations sampled at the surface by the NOAA network. This chapter describes the available data and compares this with inverted concentrations.

3.1 NOAA surface network Figure 5 shows locations of the NOAA flask network used in this study. In total 32 stations were selected based on availability of long time series, and locations not influenced by local sources. The observations from the other sites were not used in the inversion, but only used for validation purposes. These include observations from stations that were only operational for a part of the reanalysis period, or from stations under strong influence by local sources (urbanized areas). Observations from towers were excluded from both selections since these are difficult to be represented by the model. Continuous measurements were excluded too since these are not available for the entire period, but mainly after 2007.

Figure 5 - Locations of ground based stations included in the inversion (analyzed) and other locations used for validation.



3.2 Number of data values The number of observations available per month is shown in Figure 6. The stations selected for analysis provide about 100-160 observations per month during the entire period, while about 50-100 observations per month are available for validation.

Figure 6 - Number of surface observations per month used for analysis and available for validation. Tower observations and continuous measurements were excluded from these sets.

3.3 Example time series Examples of time series at one of the sites are shown in Figure 7. The panels’ show time series of CH4 concentrations at site Terceira Island in the Atlantic Ocean, just south-west of continental Europe; this site was included in the inversion. The inversions have been performed in parallel 4D-var cycles, each with a target time window of 3 years extended with 6 months spin-up and spin down; the panels show timeseries for the first four time windows. For the selected site, the a priori simulation is usually already in reasonable agreement with the observations. The a priori simulation was initialized from concentrations obtained with a coarse resolution inversion, and therefore the concentrations at the start of the window match well. Throughout the rest of the window, the a priori starts to deviate for some of the inversions, as is visible especially for the first window (2000-2003). By adjusting emissions, the 4D-var inversion is able to correct the concentrations by removing persistent biases (as seen in the first window), and by adding more variability in the time series.



Figure 7 - Example time series of CH4 surface concentrations at Terceira Island (Azores, Atlantic Ocean): observations (black), 4D-var a priori simulation (blue), and 4D-var a posteriori (red).



3.4 Timeseries of statistics Statistics over the available surface data have been collected in Figure 8. The time series show the bias (observation minus simulation) and rmse (root mean square error) over samples collected per month, for either the a priori or a posteriori simulations, and for the collection of analysis or validation sites. Although results at individual locations can be rather different, these time series give an overview of the general characteristics. The solid lines in the figure show results over observations that were included in the inversion. The a priori bias (top panel, blue lines) shows a clear seasonal behavior, with the model more often exceeding the observations during the northern hemisphere summer than during the winter. The a priori results also show a strong year-to-year variation, with a clearly larger bias during 2000-2002 which is the first 3-yearly inversion window of the “S1” stream (NOAA-only). Also the a priori rmse values (lower panel) show this variation. This might be due to the spin-up of the entire inversion system, which will be evaluated during the extension with inversions for 1991-1999 that are planned for release in 2018. The a posteriori results (red lines for stream “S1” and green lines for stream “S2”) show significant improvements. Both the seasonal and inter-annual variations in bias and rmse are to a large extent removed. The rmse values are decreased to a range of 10-15 ppb, while the a priori values reached values above 20 ppb. Only exception is the bias for the “S2” stream (NOAA+GOSAT) during its first 3-yearly inversion window (2009-2011) which is clearly positive, while it fluctuates around zero for the following years. The origin of this is unknown, but might be related to initial concentrations (obtained from a sequence of coarse resolution inversions) that are not developed enough for the first 3-yearly NOAA+GOSAT inversion. Unfortunately this cannot be corrected by extending the stream with inversions for prior years, since there is no satellite data available prior to the start of the stream. The statistics of the validation time-series show similar results for all years. The seasonal pattern visible in the a priori biases (blue dotted lines) is to a large extent removed by the inversions. The remaining biases are negative, which means that simulations under estimate observations. This can be explained from the chosen selections where all stations in urbanized areas were assigned to the validation set: simulations for these stations often under estimate the locally observed peak values due to use of grid boxes with average concentrations. For the same reason the computed rmse values are also higher for the validation set than for the analyzed set, and reach values from 20-50 ppb per month. Values computed for the “S2” stream (NOAA+GOSAT) are comparable to the values computed for the “S1” stream (NOAA-only).



Figure 8 - Time series of per-month bias and rmse between surface station observations and simulations (blue a priori, red a posteriori), for either the analysis (solid) or validation collection (dotted).



4. Comparison with GOSAT XCH4 columns Total column mixing ratios of methane (XCH4) are included in the “S2” stream of the inversions, in addition to the NOAA flask observations from the surface. This chapter describes the GOSAT product used and compares the data with simulations from the inversion system.

4.1 GOSAT XCH4 product For the inversion, the RemoteC XCH4 PROXY v2.3.8 retrieval data set is used, as produced by SRON for the ESA/CCI project (Detmers & Hasekamp, 2016). Initial validation against TCCON XCH4 observations showed mean differences less than 1 ppb, with standard deviations around 15 ppb.

4.2 TM5-GOSAT bias correction

Following Pandey et al. (2016), a bias correction is applied when comparing the GOSAT XCH4 with TM5 simulations during the inversion. The bias correction is derived from a comparison between the GOSAT XCH4

product and the S1 stream inversion using NOAA surface observations. The bias correction accounts for inconsistencies between inversions using in situ and satellite data, caused most likely by a combination of

transport model and spectroscopic uncertainties.

Figure 9 shows the value of the correction, which is defined per month and latitude band. The signs of corrections are in agreement with the comparison between the S1 results (surface-only inversion) and TCCON column observations that will be shown in Figure 16. However, where the TM5-TCCON bias swaps sign around the equator, the TM5-GOSAT bias swaps sign around 50oN. Note that the XCH4 columns of TCCON and GOSAT cannot be compared directly since both have different sensitivities with altitude; therefore, also their biases with their model simulations should not be compared one-to-one. The magnitudes of the biases are in the same range however, with a maximum overestimation of about 0.4% in the north and maximum underestimation of about -0.8% in the south. The differences between TM5 simulations and GOSAT XCH4 columns over Antarctica were found to be rather large and scattered. It was therefore decided to exclude all observations below 60oS from the inversion.



Figure 9 – Monthly and latitudinal bias corrections applied to GOSAT XCH4 observations that is used to compensate for the difference with TM5 simulations. Observations over Antarctica (below 60oS) are

excluded from the inversion.

4.3 Number of observations The number of available GOSAT retrievals per month is shown in Figure 10. The first retrievals were released in April 2009, which means that no data is available for the start of the first inversion window (2009 minus 6 months spin-up). On average about 30,000 observations are available per month. An exception is December 2014, when the instrument suffered from technical failure that was not solved until February 2015. During 2016 a steady increase in the number of available pixels is seen.

Figure 10 - Number of available GOSAT retrievals per month.

4.4 Maps comparing GOSAT columns with simulations The GOSAT XCH4 observations have been compared with their simulations. Note that these are the XCH4 “observations” after removal of the TM5-GOSAT bias as obtained from the surface-only inversion.



Figure 11 shows maps with mean observations and simulations, and the bias and rmse between them, for the a priori and the a posteriori simulations. The GOSAT retrievals are available over land and on selected paths over the oceans at equatorial and lower mid latitudes. The map with mean values of a priori simulations shows the typical latitudinal bias found for TM5 versus GOSAT: the model overestimates the XCH4 columns at northern latitudes, and underestimates at southern latitudes. A significant bias is therefore present in the a priori simulations. Note this is a bias that is left after adding the TM5-GOSAT bias correction to the GOSAT observations; this correction is defined per month and latitude, and therefore regional differences in the bias remain. The inversion assimilating the NOAA and GOSAT observations reduces the biases almost everywhere to absolute values of a few ppb. The sign of the bias becomes slightly positive in most regions, while for the a priori simulations strong negative (South America, southern Africa) and positive biases (east China, northern hemisphere oceans) were found. The reduction of the bias by the inversion also decreases the root-mean-square-error (rmse), especially at equatorial and lower latitudes.

4.5 Comparison by latitude and time The same statistics (mean, bias, rmse) have also been collected per latitude band and per month, and results are shown in Figure 12. The mean values in the observations clearly show the globally increasing trend in XCH4 in time. The increase seems stronger for recent years. This trend is also seen in the simulations. The bias in the a priori simulations shows again the latitudinal gradient with simulations exceeding the observations at northern latitudes during the northern hemisphere summer. The under estimation at southern latitudes seems stronger for 2009-2011 and 2015 than during the other years; contrary, the over estimation at northern latitudes is stronger for 2012-2014. From the mean simulations it can be concluded that the increase of concentrations in time starts too early in the model compared to what is observed. After inversion, this bias has strongly decreased for all latitudes and months. The bias has not become zero, which indicates that information from GOSAT has been used in the inversion in addition to the NOAA surface observations, since the GOSAT data actually used was bias-corrected with the results from the NOAA-only inversion. A striking feature in the bias plot after inversion is the swap of sign, with the a posteriori simulations now slightly over estimating the observations at southern latitudes. The rmse values show that the overall difference has strongly decreased after inversion. Largest differences remain at latitudes above 50o, where the uncertainty in retrievals is higher due to lower solar zenith angles and the associated weight in the inversion is smaller. Note that this result is only seen for the Northern hemisphere, since GOSAT observations below 60oS where excluded from the reanalysis completely.



Figure 11 - Statistics of GOSAT XCH4 observations (bias corrected for TM5-GOSAT differences) and/or simulations from “S2” stream (NOAA+GOSAT, 2009-2016). Top panel shows observations, in the rows below the column on the left show statistics for a priori simulations, and right column for a posteriori simulations.

Statistics over pixels collected in 1ox1o grid cells.



Figure 12 - Monthly zonal statistics over and between (TM5 bias corrected) GOSAT XCH4 observations and simulations, collected over latitude bands of 10 degrees for the “S2” stream (NOAA+GOSAT) inversion over 2009-2016. Top row shows observations, in rows below left column shows a priori simulations, and right

column a posteriori simulations.



5. Comparison with TCCON XCH4

5.1 TCCON total CH4 columns The Total Carbon Column Observing Network (TCCON) provides observations of the total CH4 column (and other tracers) using ground-based Fourier Transform Spectrometer (FTS) instruments. The reported total columns are smoothed versions of the actual columns, since the instrument is less sensitive to concentrations in the top of the atmosphere. For comparison with model profiles it is therefore essential to take the averaging kernels that are provided with the product into account. Also the water content of the atmosphere is important for correct comparison of model simulations with TCCON data. An extensive description of how to compare the TCCON observations with model simulations (Wunch et al., 2010) is available on the TCCON wiki 1 . The observed columns are expressed as column-averaged dry molar fraction (XCH4) in ppb.

5.2 TCCON network The station locations are shown in Figure 13. Sites are distributed over the globe, with the majority of the observations in the U.S. and in Europe. The measurement frequency is high (several observations per hour); we therefore averaged the observations over 1.5 hour for noise reduction and comparison with (also averaged) model simulations. The colors in the map indicate the number of available averaged observations per site. The evolution of the network is illustrated in Figure 14. After the start of the network in 2004, the amount of observations has grown continuously, with the largest number of observations available during the northern hemisphere summer.

Figure 13 - Map with locations of TCCON sites used for current validation; colors indicate number of available XCH4 samples (temporal averaged).

1 https://tccon-wiki.caltech.edu/Network_Policy/Data_Use_Policy/Auxiliary_Data

https://tccon-wiki.caltech.edu/Network_Policy/Data_Use_Policy/Auxiliary_Data



Figure 14 - Total number of (1.5-hourly averaged) TCCON XCH4 observations available per month during the reanalysis period up to 2016.

5.3 Timeseries of statistics The observed XCH4 columns have been compared with their simulated counterparts in the reanalysis. Figure 15 shows time series of monthly statistics over all available data (temporal averaged). The results for the bias show that the a priori model in general exceeds the observations, especially during the northern hemisphere summer, when the bias reaches values of 15-20 ppb. After inversion, this bias is decreased to about 5-10 ppb except for the first years of the two inversion streams when values of 10-15 ppb are obtained. Together with the bias, also the rmse decreased from 15-20 ppb for the a priori simulations to 10-15 ppb after inversion. The comparison between TCCON observations and their TM5 simulations is a second comparison of XCH4 columns, following the comparison between GOSAT observations and simulations. Although TCCON and GOSAT observations are not 1-to-1 comparable due to different sensitivities with altitude, their comparisons with TM5 simulations give insight in how the model represents the total column. As illustrated in Figure 9, a persistent bias is present between the a posteriori simulations from the NOAA-only stream and the GOSAT observations, attributed to model transport biases. With a magnitude of about 0.8%, which is equivalent to about 14 ppb, this bias correction is actually larger than the bias between TCCON and TM5 after the NOAA-only inversion seen in Figure 15. In addition it should be reminded that also the GOSAT retrievals have been calibrated (bias corrected) using the TCCON observations. A full comparison between TM5, GOSAT, and TCCON columns, before and after inversion, and with and without analyzing GOSAT data, should help to distinguish the different biases and bias corrections, and whether these are all applied correctly. Of importance is for example whether the use of the same TM5-GOSAT bias correction for each year is sufficiently valid for the entire inversion period.

5.4 Comparison by latitude and time Similar as done for the GOSAT data, statistics for TCCON XCH4 columns have been collected per latitude band of 10 degrees and per month. Figure 16 shows the results for the “S1” stream (NOAA-only) as well as the “S2” stream (NOAA+GOSAT).



The observations clearly show an increasing trend in time: between 2005 and 2016, the observed XCH4 columns increase with about 100 ppb. The same trend is seen in the model simulations. The plots of the bias values show that the model has a persistent bias in the north-south gradient, with the model over estimating the total columns at the northern hemisphere and under estimating at the southern hemisphere. Since most TCCON sites are at the northern hemisphere, this explains the over-all positive bias of TM5 towards TCCON seen in Figure 15. The bias decreases after inversion, but does not disappear completely. The biases for “S1” and “S2” streams are very similar, with the “S2” stream (NOAA+GOSAT) having slightly higher biases during the first years of the inversion. The same is seen for the rmse: although it decreases after inversion, a substantial error remains, especially for the first years of the NOAA+GOSAT inversion.



Figure 15 - Bias (top) and rmse (bottom) between TCCON observed and simulated XCH4 columns per month during the reanalysis period.



Figure 16 - Statistics per month and 10 degree latitude band for observed and simulated XCH4 at TCCON stations. The top panel shows mean observed values. The left column shows a priori simulated mean for the

“S1” stream (NOAA-only), and bias and rmse between observation and simulations; the middle column shows the corresponding a posteriori results. Right column shows a posteriori results for the “S2” stream

(NOAA+GOSAT) for the time range of that inversion (2009-2016).



6. Comparison with aircraft observations One of the “Key Performance Indicators” (KPI) formulated for the reanalysis is based on the a posteriori difference between simulated concentrations of CH4 and aircraft observations in the free troposphere.

6.1 Aircraft observations Aircraft observations including CH4 are available from various campaigns. For the current inversions (reanalysis 2000-2016), flight observations have been collected from NOAA, from 4 locations in Europe (Bialystok, Hungary, Orleans, and Griffin), and HIPPO. The locations for which observations are available are shown in Figure 17. Data from other campaigns (CARIBIC, CARVE, CONTRAIL, IAGOS, AMAZONICA) is currently collected too, and will be used for validation purposes in future releases.

Figure 17 - Locations of flight campaigns used for validation.

6.2 KPI values Figure 18 shows the bias and standard deviation between the aircraft observations and a posteriori simulations with the TM5 model using optimized fluxes. Only data valid for the free-troposphere was used, which was here defined as all samples between the boundary layer (from ECMWF meteorological data) and 8 km altitude (above sea level). A maximum value of 10 ppb was defined as the KPI for both bias and standard deviation. The upper panel shows computed biases and standard deviations when all available data is included in the comparison. The (absolute) biases are all below the maximum value of 10 ppb, with most computed values below 5 ppb. Differences between streams “S1” (NOAA-only) and “S2” (NOAA+GOSAT) are present, but for both streams the bias values are below the required maximum. Results for years 2009-2011 are strongly influenced by the HIPPO data which adds many observations in the southern hemisphere to the comparison, and due to the latitudinal bias in TM5 the model under estimates concentrations here.



The standard deviation is on average about 5 ppb above the target value of 10 ppb. The high standard deviations are caused by strong variations present in observations above continental source regions. When the comparison is limited to ocean pixels only (lower panel of the figure), the standard deviations decrease and are up or below the target value during 50% of the years. Based on the current results, a further reduction of the standard deviation as it is computed now is probably difficult to obtain. The high frequency variations in the air craft data cannot be represented well by the model, which is (compared to the observations) smoothed in space and time. Smaller standard deviations could only be expected if the comparison would be based on averaged observation over model grid cells and time steps; the KPI should then be more precisely defined as “bias and standard deviation between grid box averaged air craft observations and simulations”. The results for such a definition are currently investigated to guide a more detailed discussion on the definition of this KPI.

Figure 18 - Bias and standard deviation between aircraft observations of CH4 in the free troposphere and a posteriori simulations for inversion streams “S1” (NOAA-only) and “S2” (NOAA+GOSAT). Lower panel shows

same values with data selection limited to oceans only.



7. Summary and outlook The inverted CH4 fluxes described in this report are part of the CAMS/GHG CH4 flux inversion 2000-2016 (stream 1, using surface observations only) and 2009-2016 (stream 2, using surface and GOSAT satellite observations). These inversions form the basis of an extension with 1991-1999 (stream 1) and 2017 (both streams) that are planned for release in 2018. The surface+satellite stream requires application of a bias correction on the GOSAT XCH4 column data, to compensate for a persistent bias between the original satellite data and TM5 model simulations. The bias correction accounts for a latitudinal gradient in the CH4 total columns that is too steep in the model. It is unlikely that this latitudinal bias is not in some way also influencing the surface-only inversion. Continuation of the work performed on understanding the origin of this bias and on model improvements to correct for it therefore remains essential. In addition, a full comparison of available XCH4 data from all possible sources (TCCON, GOSAT, TM5, with or without bias correction, and a priori or posteriori) should be performed to better understand the biases and to check the application of corrections. Comparisons have been made between the a posteriori simulations and observations. The results can be summarized as follows.

For the surface-only stream, the bias with surface observations is less than 5 ppb for the analyzed data (collected per month), and about 10-20 ppb for the other data (including data from locations that are difficult to represent by the model). For the surface+satellite inversion, the bias with analyzed data is about 5-10 ppb during the first years of the inversion.

The bias with bias-corrected GOSAT XCH4 columns used in the inversion is less than 3 ppb. Largest remaining differences are left above 50oN where low solar zenith angles increase the retrieval error and the associated weight in the inversions is lower.

The bias between total XCH4 columns and observations from the TCCON network (1.5 hourly averaged) is usually less than 10 ppb per month, with some exceptions for the early years of the inversions.

The bias between CH4 concentrations and aircraft observations in the free troposphere is less than 5 ppb for most years. Standard deviations are around or close to 10 ppb over the oceans, and around 15-20 ppb over all data.

For both the surface-only and the surface+GOSAT inversions, the time series of inverted CH4 emissions show an increased amplitude of the seasonal pattern. Higher total emissions during northern hemisphere summer are attributed to higher wetland and rice emissions. Lower winter values are attributed to lower anthropogenic emissions in especially East Asia; this change is smaller when the GOSAT data is included in the inversion (stream 2). For the anthropogenic emissions, this actually leads to a shift in the seasonal cycle with the northern hemisphere winter emissions estimated to be lower than the northern hemisphere summer emissions. At the moment it is not decisive that these changes indicate differences between a priori and actual anthropogenic emissions due to (for example) changes in agricultural practices or energy use. Instead, the estimated difference in anthropogenic emissions could also compensate for errors in a priori natural emissions from for example wetlands or biomass burning. A recent inversion study over Europe (Bergamaschi et al. ,



2017) also reported higher emissions in summer, which are attributed to increased wetland emissions. An update of the wetland emission inventory is therefore considered. In addition, a preliminary study (Sander Houweling, personal communication) suggested that the current inversion corrects the isotopic weight of the emissions into the direction indicated by a recent review of methane emissions from biomass burning (Worden et al., 2017), but not strong enough yet. Also here an update the a priori biomass burning inventory to for example CAMS/GFAS or GFED v4.1 might improve the inversions. For both inversion streams the validation results for the first years are sometimes slightly worse than what is achieved later on. This could be related to a spin-up that is too limited. For the first years of the surface-only inversion this will be investigated when the inversion is extended with the years 1991-1999. The current production chain does not provide error estimates of the state variables yet, which are here the monthly maps of emissions in four different categories. A new optimizer based on conjugate gradients will be used in a future release to provide these.



8. References Alexe, M., Bergamaschi, P., Segers, A., Detmers, R., Butz, A., Hasekamp, O., … Kort, E. A. (2015).

Inverse modelling of CH4 emissions for 2010–2011 using different satellite retrieval products from GOSAT and SCIAMACHY. Atmospheric Chemistry and Physics, 15(1), 113–133. https://doi.org/10.5194/acp-15-113-2015

Bergamaschi, P., Karstens, U., Manning, A. J., Saunois, M., Tsuruta, A., Berchet, A., Vermeulen, A. T.,

Arnold, T., Janssens-Maenhout, G., Hammer, S., Levin, I., Schmidt , M., Ramonet, M., Lopez, M., Lavric, J., Aalto, T., Chen, H., Feist, D. G., Gerbig, C., Haszpra, L., Hermansen, O., Manca, G., Moncrieff, J., Meinhardt, F., Necki, J., Galkowski, M., O'Doherty, S., Paramonova, N., Scheeren, H. A., Steinbacher, M., and Dlugokencky, E.: Inverse modelling of European CH4 emissions during 2006–2012 using different inverse models and reassessed atmospheric observations, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-273, in review, 2017.

Chevallier, F., P. Bergamaschi, D. Brunner, L. Feng, S. Houweling, T. Kaminski, W. Knorr, J. Marshall, P.

I. Palmer, S. Pandey, M. Reuter, M. Scholze, and M. Voßbeck, Climate Assessment Report for the GHG-CCI project of ESA’s Climate Change Initiative, pp. 96, version 4, 28 March 2017, 2017. http://www.esa-ghg-cci.org/?q=webfm_send/385

Detmers, R., & Hasekamp, O. (2016). Product User Guide (PUG) for the RemoTeC XCH4 PROXY GOSAT

Data Product v2.3.8. Pandey, S., Houweling, S., Krol, M., Aben, I., Chevallier, F., Dlugokencky, E. J., … Röckmann, T. (2016).

Inverse modeling of GOSAT-retrieved ratios of total column CH4 and CO2 for 2009 and 2010. Atmospheric Chemistry and Physics, 16(8), 5043–5062. https://doi.org/10.5194/acp-16-5043-2016

Segers, A. J., & Houweling, S. (2017). CAMS/D73.2.5.5 - Description of the CH4 Inversion Production

Chain. Worden, J. R., Bloom, A. A., Pandey, S., Jiang, Z., Worden, H. M., Walker, T. W., … Röckmann, T.

(2017). Reduced biomass burning emissions reconcile conflicting estimates of the post-2006 atmospheric methane budget. Nature Communications, 8(1), 2227. https://doi.org/10.1038/s41467-017-02246-0

Wunch, D., Toon, G. C., Wennberg, P. O., Wofsy, S. C., Stephens, B. B., Fischer, M. L., … Zondlo, M. A.

(2010). Calibration of the Total Carbon Column Observing Network using aircraft profile data. Atmospheric Measurement Techniques, 3(5), 1351–1362. https://doi.org/10.5194/amt-3-1351-2010

http://www.esa-ghg-cci.org/?q=webfm_send/385


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