Validation of the CH surface flux inversion - reanalysis ... · CAMS73_2018SC1_D73.2.4.1-2019_202021_validation_CH4_1990-2018_v1 Page 11 of 40 2. Optimized emissions The inversion

ECMWF COPERNICUS REPORT

Copernicus Atmosphere Monitoring Service

Validation of the CH4 surface flux inversion - reanalysis 1990-2018

Issued by: TNO / Arjo Segers

Date: 24/01/2020

Ref: CAMS73_2018SC1_D73.2.4.1-2019_202021_validation_CH4_1990-2018_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_2018SC1_D73.2.4.1-2019_202021_validation_CH4_1990-2018_v1 Page 3 of 40

Contributors

TNO Arjo Segers

VU AMSTERDAM Sander Houweling



Table of Contents

1. Production of inversions 7

1.1 Summary of changes 7

1.2 Inversion chain 7

1.3 Emission inventories 9

2. Optimized emissions 11

2.1 Timeseries of yearly emissions 11

2.2 Timeseries of monthly emissions 14

2.3 Emission maps 17

2.4 Emissions by latitude and time 19

3. Comparison with NOAA surface observations 21

3.1 NOAA surface network 21

3.2 Number of data values 21

3.3 Timeseries of statistics 22

4. Comparison with GOSAT XCH4 columns 25

4.1 GOSAT XCH4 product 25

4.2 TM5-GOSAT bias correction 25

4.3 Number of observations 26

4.4 Maps comparing GOSAT columns with simulations 27

4.5 Comparison by latitude and time 27

5. Comparison with TCCON XCH4 30

5.1 TCCON total CH4 columns 30

5.2 TCCON network 30

5.3 Timeseries of statistics 31

5.4 Comparison by latitude and time 32

6. Comparison with aircraft observations 34

6.1 Aircraft observations 34

6.2 KPI values 34

7. Summary and outlook 37

8. References 39





Introduction This report describes the CAMS CH4 surface flux inversion for the reanalysis period 1990-2018. The production is described in detail in the latest version of:

Description of the CH4 Inversion Production Chain which is available from the CAMS website, and updated together with this report. The data sets described in this report have release tags “v18r1” and “v18r1s”, since these are the first releases of the CH4 reanalysis product that end with 2018. In summary:

• Release “v18r1” covers the period 1990-2018 and is based on inversion of surface observations only.

• Release “v18r1s” covers the period 2009-2018 and uses, in addition to the surface observations, also GOSAT satellite observations.

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. Release tags and filenames of the latest available products 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.

Release tag Released data files description

v18r1 (surface obs.) 1990-2018

3o×2o globally

2020-01 z_cams_l_cams73_yyyymm_v18r1_fg_sfc_mm_ch4_flux.nc z_cams_l_cams73_yyyymm_v18r1_fg_sfc_dm_ch4_conc.nc z_cams_l_cams73_yyyymm_v18r1_fg_sfc_6h_ch4_conc.nc z_cams_l_cams73_yyyymm_v18r1_fg_sfc_6h_ch4_col.nc

4D-var first guess (a priori)

z_cams_l_cams73_yyyymm_v18r1_ra_sfc_mm_ch4_flux.nc z_cams_l_cams73_yyyymm_v18r1_ra_sfc_dm_ch4_conc.nc z_cams_l_cams73_yyyymm_v18r1_ra_sfc_6h_ch4_conc.nc z_cams_l_cams73_yyyymm_v18r1_ra_sfc_6h_ch4_col.nc

4D-var reanalysis (a posteriori)

v18r1s (surface + GOSAT) 2009-2018

3o×2o globally

2020-01 z_cams_l_cams73_yyyymm_v18r1s_fg_sfc_mm_ch4_flux.nc z_cams_l_cams73_yyyymm_v18r1s_fg_sfc_dm_ch4_conc.nc z_cams_l_cams73_yyyymm_v18r1s_fg_sfc_6h_ch4_conc.nc z_cams_l_cams73_yyyymm_v18r1s_fg_sfc_6h_ch4_col.nc

4D-var first guess (a priori)

z_cams_l_cams73_yyyymm_v18r1s_ra_sfc_mm_ch4_flux.nc z_cams_l_cams73_yyyymm_v18r1s_ra_sfc_dm_ch4_conc.nc z_cams_l_cams73_yyyymm_v18r1s_ra_sfc_6h_ch4_conc.nc z_cams_l_cams73_yyyymm_v18r1s_ra_sfc_6h_ch4_col.nc

4D-var reanalysis (a posteriori)

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



1. Production of inversions

1.1 Summary of changes Compared to the v17r1(s) releases, the production chain received the following major updates:

• the a priori emission inventories have been updated to latest available versions;

• the input meteorology is now based on ECMWF’s ERA5 reanalysis instead of ERA-Interim;

• the inversions are now performed using a single series of smaller inversions instead of combined from multiple shorter series.

Because of their relevance for interpretation of the results, the changes in inversion chain and emissions are summarized below; details can be found in the Production Chain report.

1.2 Inversion chain The production stream consists of two steps, illustrated in Figure 1. First a series of coarse resolution inversions (6o×4o degrees, 25 layers) is performed sequentially, which provide initial conditions for a series of high resolution inversions (3o×2o degrees, 34 layers) that are performed in parallel. For the current release, the inversions are performed for the full time range 1990-2018 (or 2009-2018 for the inversion including satellite data). This is different from previous releases (up to v17r1(s)), which were created as a combination of inversion series over shorter periods. The coarse resolution inversions use yearly target windows, extended with 6-month spin-up/spin-down. These inversions optimize both emissions and initial concentrations. The high resolution inversions use 3-yearly target windows (or 1 or 2 at the end of the series), extended with 6-month spin-up/spin-down periods. Each inversion window uses initial concentrations obtained from concentration fields of a coarse resolution inversion, and optimizes only emissions. The inversions including GOSAT data rely on the surface-only inversions in two ways. First, the series of coarse resolution inversions is initialized with a concentration field from a surface-only inversion. Second, a bias correction for the difference between model simulations and GOSAT observations is used, based on statistics obtained from the surface-only inversion.



Figure 1 Overview of inversions used for “v18r1” and “v18r1s” releases. Sequential series of coarse resolution inversions with yearly target windows (blue) provide initial conditions for high resolution inversions over (preferably) 3-yearly target windows (green). The final product is the result of the high resolution inversions marked with solid green boxes.



1.3 Emission inventories In comparison to v17r1, the current v18r1 production received a major update of the a priori emission inventories. Table 2 shows an overview of the inventories used for the various years of the inversion; for details we refer to the Production Chain report. The inversion system optimizes emissions in four categories, each collecting emissions from source sectors that are to some extent spatially correlated. In summary, for wetland CH4 emissions a simulation with LPJ-wsl is used (Zhang et al., 2018). Rice and anthropogenic emissions are taken from EDGAR v4.3, which provides yearly inventories up to 2012. For this inventory a seasonality (monthly variation per grid cell) is available for the year 2010, which could be used for all years. Note that for CH4 emissions from rice fields this profile was found to be incorrect, and has therefore been replaced by the Matthews et al. profile used before. For years after 2012, the inventories are extrapolated using yearly growth factors taken from fossil fuel and agricultural statistics. Biomass burning emissions are taken from ACCMIP-MACCity up to 2002, and from GFAS from 2003 onwards. Climatologies are used for the remaining sources (oceans, wild animals, and termites), as well as for the soil sinks. Table 2 - Overview of a priori emission inventories used for the 1990-2017 inversions, with spin-up from 1989 and spin-down to 2018. The colors represent the different emission super-categories that are optimized by the inversion.

Category Period Source

Wetlands

< 1990 (1990)

1990-2017 LPJ-wsl

> 2017 (2017)

Rice ≤ 2012 EDGAR v4.3 with Matthews seasonality

≥ 2013 EDGAR v4.3 with Matthews seasonality, extrapolated

biomass burning ≤ 2002 ACCMIP-MACCity

≥ 2003 GFAS

other anthropogenic

≤ 2012 EDGAR v4.3 with 2010 seasonality

≥ 2013 EDGAR v4.3 with 2010 seasonality, extrapolated

oceans climatology Lambert

wild animals climatology Olson

soil sink climatology Ridgwell

termites climatology Sanderson



Figure 2 – Illustration of a priori emission inventories used during the 1990-2018 inversions.



2. Optimized emissions The inversion wet

2.1 Timeseries of yearly emissions Figure 4 shows timeseries of yearly global emissions for the four emission categories considered in the inversions, as well as the global total (bottom panel). Each of the plots shows the global a priori emissions (black), and the inversion results using either surface observations only (v18r1, red) or also the satellite data (v18r1s, green). Since the a priori inversions have received a major update, also results for the previous v17r1(s) release are added (dashed lines). The bottom panels show the global total emission time series. Up to about 2010, the a priori estimates are substantially lower in the new inventory (black, solid) than in the previous (black, dashed), up to about 2010. The results per source category show that this is caused by lower “wetland” (first panel) and especially lower “other” emissions (fourth panel). The “other” emissions consist mainly of the EDGAR anthropogenic emissions, and the inventory is substantially lower during the earlier years of the period. The differences between the previous (v4.2) and latest (v4.3) EDGAR inventories are illustrated in Figure 3, which shows the global total emissions in the inventories per source category. Especially the emissions from sectors related to energy production (lowest two bars) have decreased in the new inventory, which is due to lower estimates for CH4 leakage during power production and from pipelines (Antoon Visschedijk, personal communication). The lower anthropogenic a priori emissions are in agreement with a posteriori emissions: for 1990-2005, the analyzed “other” emissions for both the current release and previous release are similar to the new a priori. For the years after 2005 however, the analyzed “other” emissions become lower than the new a priori, when only surface observations are analyzed. When the satellite data is included too (green lines), the analyzed “other” emissions are rather similar to the new a priori values. For the “wetlands” category the difference between old and new release are smaller. The a priori wetland emissions were for the v17r1 release based on a climatology, with a global total value rather similar to the new LPJ-wsl numbers (black lines in 1st panel). When only surface observations are analyzed (red lines), the global sums are increased with roughly 30 Tg/year. When also satellite data is analyzed, the wetland emissions are however decreased with about 10-20 Tg/year for 2009-2015, or they are comparable with the a priori for the most recent years. These results are in general similar to what was obtained with in the v17r1(s) inversions for 1990-2006, for recent years the differences are larger. For the emissions from rice fields (2nd panel) the estimated emissions in the new release are very similar to those obtained before. The global sum of the new a priori is almost equal to the previous estimate. Also the analyzed emissions are similar, and are about 10 Tg/year higher for all years, both for the surface-only and the surface+satellite inversions. The changes applied to emissions from biomass burning (3rd panel) are rather small: the global sum of the analyzed emissions differs from year to year but is close to a priori estimates. This is for example clearly visible for the years after 2012, where the v17r1 a priori was using a climatology without year-to-year variation, and this was also the case for the corresponding analysis. The new a



priori from GFAS shows strong variations during these years however, and these are followed by the analysis. The time series for the total emissions (bottom panel) shows a small but steady growth of emissions over the entire time period. The increase in the a priori emissions is stronger than in the analysis due to higher emissions in the 1990’s and early 2000’s. This increase is almost entirely due to higher wetland emissions, which are increased over the entire time range when only surface observations are analyzed, or hardly changed when satellite data is analyzed too. The impact of increased wetland emissions in the surface-only inversion is for the later years compensated by lower “other” (anthropogenic) emissions. The net result is that for the later years the global sum of the analyzed emissions is almost the same as the a priori since the changes applied to individual categories cancel each other. Only for 2018 the a priori is slightly higher than the inversion results, but since this a priori is based on extrapolation of growth factors its value is rather uncertain. Figure 3 - Summary of CH4 emissions in 1990 following EDGAR inventories v4.2 and v4.3.



Figure 4 - Timeseries of yearly emissions for different source categories. The dashed lines show the corresponding results for the previous v17r1(s) release.



2.2 Timeseries of monthly emissions The inversion system optimizes emissions on monthly temporal resolution, and therefore it is useful to look at the resulting seasonal cycles. Figure 5 shows time series of monthly global emissions before and after inversion, which shows the month-by-month variation on top of the year-to-year variation shown in Figure 4. Different colors are used for inversion with surface observations only (red) and including satellite data too (green); dashed lines show the similar results for the previous release. For the wetlands category, the update of the a priori emissions from a climatology to LPJ-wsl simulations has led to a shift in the global emission peak from May to August (first row, right panel). This seems in agreement with the reanalysis result, which places the maximum at July/August in both the previous as well as the current release. The amplitude of the emission peak is larger than in the a priori, which leads to increased wetland emissions already seen in Figure 4. The timeseries also show a persistent second maximum in March, which is partly also present in the new a priori inventory. In some years (1992, 1997) this n.h. spring maximum has the same amplitude as the later n.h. summer maximum, although for 1992 this is estimated to be rather low. When GOSAT observations are included in the inversion, the amplitudes of the summer peaks are somewhat lower for most years, although still higher than the a priori. For the first months of 2009 the inversion with GOSAT strongly decreases the wetland emissions, which is partly also visible for other years. The effect is most visible for 2009 however, in spite of GOSAT observations being available only after April 2009. This suggests that the spin-up of the surface+satellite steam needs to be improved to have a smoother overlap between the streams. The analyzed rice emissions show an increased n.h. summer peak compared to the a priori inventory. Especially for the 1990’s the emissions are increased, with the maximum obtained for 1990. This pattern is very consistently present in the current as well as the previous release. Also when satellite data is included in the inversion, the increase in rice emissions is seen, although the amplitude is smaller. The month of the emission peak slightly shifts from August/September to July/August with the new inventory, as it also changed from May to July/August in the previous release. The strong increase for 1990 was for the previous release partly attributed to the need for combining inversions over 1-3 year into a single release, but since it is still visible in the current release that uses 3-year inversions, the impact of this seems limited. The smaller number of surface observations available for the early 1990’s might play a role in case the available network is more sensitive to regions with rice cultivation. The prior biomass burning emissions show strong inter-annual variations. Highest emissions are estimated for 1997 when Indonesia suffered from strong fire events. The total amount of emissions as well as the seasonal cycle hardly changed with the new a priori inventory. The new GFAS emissions prescribe a stronger peak in March than the previously used GFED data (which was partly climatological), but this is however not confirmed by the a posteriori results that do not show a n.h. early spring peak at all. Apart from this peak the a posteriori estimates follow the pattern of the a priori inventories. Most significant changes in amplitude are seen during the 1990’s and the years 2002 and 2012. For 2018 a relative low amount of biomass burning emission is estimated by the a priori and the reanalysis runs. The year 2019 that will be part of the next release will probably show higher emissions due to the severe burning events in the Amazon and boreal Asia, and it will be interesting to see how the inversion deals with this.



The largest changes (absolute as well as relative) are seen for the ‘other’ (mainly anthropogenic) emissions. The new EDGAR v4.3 inventory has introduced an enhanced seasonal cycle compared to the previous a priori, which included month-to-month variations only from the soil sink contribution. The new seasonal cycle is also seen in the reanalysis results, both for the surface-only and surface+satellite stream. It should be noted here that the ‘other’ category is the only one for which a temporal correlation is defined (with length scale parameter of 9.5 months), which makes this category more closely follow the a priori temporal variability. A remarkable feature of the previous release(s) was that in spite of this temporal correlation, for many years the global seasonal cycle was changed from a peak in winter to a peak in summer. Or in other words, the inversion decreased the ‘other’ emission in n.h. winter, which lead to a yearly maximum in summer. A decrease of ‘other’ emissions in n.h. winter is also seen in the new release, but due to the new seasonal cycle the net result still shows a maximum in March. The amplitude of this March-maximum is however much lower than what is present in the a priori, and is followed by a clear minimum in May. The decreased emissions in the first months of the year are placed in Asia, as will be shown by the spatial maps in section 2.3. As seen in the previous section, including GOSAT observations leads to higher estimates of ‘other’ emissions compared to the surface-only inversions; the seasonal profile is rather similar however. For the total emissions (all categories together), the seasonal pattern after inversion is very similar to the previous results, in spite of the major update of the a priori inventories. The reanalysis result therefore seems stable with respect to changes in the inventories. A strong July/August peak is present due to wetland and rice emissions, with a second peak in March from the other (anthropogenic) emissions. The use of satellite data in the inversion hardly changes the seasonality in the total emissions, although differences are present for the individual categories.



Figure 5 - Time series of monthly global emissions for the optimized categories and the total (left), and seasonal cycles averaged over time range (1990-2018 for v18r1, and 2009-2018 for v18r1s). Dashed lines are results obtained with the previous v17r1 release.



2.3 Emission maps For an overview of the spatial changes, Figure 6 shows maps of analyzed emissions as well as analysis increments. The left and middle columns show the analyzed emissions and analysis increment for the surface-only stream (v18r1, averaged over 1990-2018), while the right column shows the analysis increment for the surface+satellite stream v18r1s, 2009-2018). The first row of the figure shows the maps for the wetland category. 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 major wetland regions of the Amazon, Congo, and Ganges basins. South-America and Africa. The differences between surface-only and surface+satellite inversions are small. Rice emissions are increased in both inversions. The increments are strongest over China, where both emissions are strong and NOAA observation sites used in the inversion are present. Rice emissions in India and south-east Asia are on average hardly adjusted. Changes in biomass burning emissions are in general small. In both inversion streams, higher biomass burning emissions are estimated over Indonesia and the Congo basin, while they are decreased over the Ganges delta and the far-south-east of Siberia. A remarkable difference is however that the use of satellite data introduces stronger fire emissions in north-central Siberia. Although the vegetation here could burn severe, it cannot be excluded that these increases should actually be attributed wetland emissions which are also present here. The ‘other’ (mainly anthropogenic) emissions are strongly decreased over Asia, especially in China but also over India. This is seen for both streams. Over China, the decreased ‘other’ emissions contrast with the increased ‘rice’ emissions, but since these changes are attributed to different seasons they do not exclude each other. The surface-only inversion also shows a small increase in emissions over Europe (incl. Russia) and the eastern U.S., which originate from increases imposed for the 1990’s (which are not included in the surface+satellite inversions). In contrast, the ‘other’ emissions of the British islands and around Uruguay are slightly decreased. The total changes in emissions are dominated by the anthropogenic sources, and therefore show similar patterns with lower emissions in Asia and higher emissions over Europe and the eastern U.S. Americas. In addition, also the higher wetland emissions in South America and Africa are visible in the total, especially in the surface-only stream which includes the 1990’s.



Figure 6 - Maps of average a posteriori emissions for NOAA-only inversion over 2009-2018 period (“Stream 1”, left), and corresponding changes in emissions with respect to a priori estimate (middle). Right column shows changes in emissions when GOSAT observations are included too (“Stream 2”).



2.4 Emissions by latitude and time Figure 7 shows emissions as function of latitude and month. The left column shows the a posteriori emissions from the surface-only inversion over 1990-2018 for each of the four emission categories and the total, and the middle column shows the corresponding inversion increments. The right column shows the analysis increment for the surface+satellite inversion, which has no data prior to 2009. Clear annual cycles are present for the emission categories wetlands, rice, and biomass burning. These are seen not only in the a posteriori emissions but also in the emission increments that were introduced by the inversion. Although additional temporal variability has been introduced with the new a priori inventories, this indicates that even stronger seasonal cycles could still be useful. An option for this might be to relax the temporal correlation defined for changes in the ‘other’ emissions; these show now a very stable pattern, but this might change if more freedom is allowed. The wetland emissions show strong oscillating changes around 30o N (Ganges delta) and 60o N (Siberia and Canada), with higher zonal total emissions earlier in the year and lower emissions later. This is in agreement with the shift of the wetland emission peak from August/September to July/August. The changes in tropical wetland emissions are more variable within the years. The overall strongest changes in the 1990’s for the tropical wetlands, and above 60o N for recent years; the latter might be an indication of melting wetlands. Also the changes in rice emissions show an oscillating pattern. Emissions increase during the n.h. summer in China, and decrease in autumn (for the surface-only inversion), and also in spring when satellite data is used too. The changes are strongest during the early 1990’s, and also 2005 received a major increase. Although the overall adjustment of biomass-burning emissions is small, the changes applied to the individual grid locations where fires took place could be strong. In general, the inversions decrease the fire emissions around 20oN (south-east Asia), and increase emissions in the tropics during the 1990’s and in northern boreal regions during recent years. Largest emissions and emission changes are seen for the ‘other’ category (mainly anthropogenic). Most striking is the decrease of emissions at 30o N (China and India) that is suggested by the inversion. A decrease in ‘other’ emissions is also seen around 50o N (Europe) between 2002 and 2010, while a strong increase is seen here for the 1990’s This might be related to an increases of anthropogenic emissions that has been estimated for Russia in the 1990’s, when economic changes had a strong impact on emissions (Reshetnikov, Paramonova, & Shashkov, 2000). Also above 60o N significant decreases are seen up to 2011 at two different latitudes, which correspond to Prudhoe Bay at the north coast of Alaska and the Severnaya Zemlya archipelago north of Siberia; the strong decrease at these latitudes might indicate a bias in the model for very high latitudes. When the satellite data is included in the inversion, the analysis increments are less strong but show similar patterns. The analysis increments in the total emissions (sum over all categories) are dominated by the decreased ‘other’ emissions at 30o N. However, also the increased wetland emissions in the tropics for the 1990’s are clearly visible. The total changes in emissions are very similar between the surface-only and surface+satellite inversions.



Figure 7 - Zonal total emissions per month after inversion of surface observations (left) and corresponding analysis increments (middle) for 1990-2018 period. Right column shows similar analysis increment when satellite data is inverted too (only available from 2009 onwards).



3. Comparison with NOAA surface observations The analyzed surface observations 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 8 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 8 - 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 9. The stations selected for analysis provide about 75 observations per month in 1990, increasing to about 120 per month around and 2000, and about 140 for recent years. For validation less than 20 observations per month are available in 1990, but the amount increased steadily to about 75 around 2010. The amount of flask data for validation seems to decrease over the last years, which might be a consequence of replacement by continuous measurements.



Figure 9 - 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 Timeseries of statistics

Timeseries of statistics over the collection of observations have been collected in Figure 10. Each value in a time series is obtained by collecting values during a month and over all analysis or validation sites.

The top panel shows average concentrations in the observations (dots) or simulations with a free running model (lines). The black dots and solid lines denote averages over the analysis sites, while the gray dots and dashed line show the same over the validation sites. The red lines in this and following panels are taken from a model simulation following the “stream 1” configuration (surface-only inversion), while the green lines correspond to the “stream 2” configuration (surface+satellite, which provides actually the same model run). The initial concentration for 1990 was taken from a concentration field that was scaled towards the observations, and model simulations are therefore close to the observations. But while the observations show a steady increase in time, the simulations start decreasing with about 5 ppb/year on average. A minimum is reached around 2002, after which simulations start to increase and almost catch up with the observations in 2018; the remaining difference is about 20 ppb. growth rate in the model is therefore either (or both) due to emissions that are not strong enough, or sinks that are too strong. The upgrade to EDGAR v4.3 has indeed led to lower anthropogenic emissions during the 1990’s, but since these are based on latest available knowledge, this update should be rejected immediately. It makes more sense to first look at the atmospheric sinks, since these are currently based on climatological values obtained from model simulations. Year-to-year or just decadal changes in the destruction rates are therefore not included yet, while these could have a significant impact on CH4 lifetime (Bândă et al., 2014; Bândă et al. 2016). An update of the sink terms is under investigation for the next release.

The second panel of the figure shows the averaged simulations at the NOAA sites for the reanalysis runs. As expected, the simulated concentrations follow the analyzed observations very well. The strong increase in concentrations during the 1990’s and after 2008 is reproduced in the analysis. Differences are especially seen in 1990 and 1991, where the inversion does not seem to be able to



capture the very low n.h. summer minimum. For the recent years from 2015 onwards, the inversion is also not able to completely reproduce the high winter values. For the validation sites the model seems also able to capture the general changes in concentrations, except for the high concentrations observed up to 2012 and in 2018. When satellite data is included in the inversion, the results are very similar. An exception is formed by the first 3 years of this stream (2009-2011 inversion), which shows a small overestimation especially for 2009. A longer spin-up period might be useful here to smoothen the connection between surface-only and surface+satellite inversion. The same findings are reflected in the bias (3rd panel) and rooted-mean-square error timeseries (bottom panel). For the surface-only inversion, the absolute bias at the analyzed sites is about 2-3 ppb only, with an rmse around 10 ppb, and these numbers are stable throughout the entire inversion period. For the validation sites these numbers are higher: before 2002, the absolute bias reaches numbers of 10-15 ppb, while after 2002 the bias becomes negative (analysis is lower than observations) with values up to -20 ppb; the rmse reaches values of 20-50 ppb. For the surface+satellite inversion, similar values are obtained for most years, with only for the initial 2009-2010 years a higher bias and rmse. The numbers obtained here are comparable with the a posteriori numbers obtained for the previous release. The maps right of the bias and rmse panels show the values over the 2009-2018 period at each of the analyzed sites; the outer circles show the result for the surface-only inversion, and the inner circles show the result when satellite data is included too. The map with the biases shows that for most sites the simulations are on average very close to the observations. In most locations the bias is slightly higher when satellite observations are included in the inversion, which is expected since it is unlikely that the surface and total column always point towards the same corrections. The rmse values are lowest for the southern locations where concentrations hardly fluctuate. The differences in rmse between the two streams is for most sites rather small. The maps also look very similar to what was obtained for the previous release. Exception is Barrow at the north coast of Alaska, which shows a significant higher rmse; this could be explained from the presence of a nearby strong emission source in the new EDGAR v4.3 inventory.



Figure 10 –Statistics for concentrations at surface stations for a posteriori simulations. Left column: time series of per-month concentrations and simulations (1st and 2nd panel), and bias and rmse between surface station observations and simulations (3rd and bottom panel). Solid lines are results for analyzed sites, while dotted lines are results for validation sites. Right column: maps with bias and rmse values per station over the period 2009-2018 for v18r1 (surface only, outer circles) and v18r1s (surface+satellite, inner circles).



4. Comparison with GOSAT XCH4 columns Total column mixing ratios of methane (XCH4) observed by GOSAT are analyzed in the “Stream 2” inversions, in addition to the NOAA flask observations from the surface. This chapter describes the GOSAT product that is used, and compares the observations with simulations from the inversion systems.

4.1 GOSAT XCH4 product For the inversion, the RemoteC XCH4 PROXY 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. For all inversion windows data from version v2.3.9 has been used; this in contrast to the previous release that was formed as a combination of inversions in which some used an earlier version of the product version.

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 11 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 “Stream 1” results (surface-only inversion) and TCCON column observations that will be shown in Figure 17. 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 11 – 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 12. 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-month spin-up). On average about 30,000 observations are available per month up to 2015, and up to 40,000 per month for recent years. During some months the instrument is configured in a targeting mode, in which specific locations of interest are observed multiple times from different angles; these results in a larger number of observations. In previous releases these observations were accidently removed following a check on duplicates, but are now included again. Less data is available for December 2014 and January 2015, when the instrument suffered from a technical failure that was not solved until February 2015. Figure 12 - 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 13 shows maps with mean observations and simulations, and the bias and rmse between them, for the a posteriori simulations of the surface-only inversions (left panels), and the inversions actually analyzing the GOSAT data. The GOSAT retrievals are available over land and on selected paths over the oceans at equatorial and lower mid latitudes. The maps with observations (top panel) and simulations (first row) show the zonal gradient in CH4 concentrations (columns) with lower values towards the south pole. At first glance the difference between simulations and observations is small, which is expected since the observations have been bias-corrected for the TM5-GOSAT differences. Since this correction is defined per month and latitude, regional differences in the bias remain. For the surface-only inversion, the a posteriori bias (third row, left panel) is at most locations not more than a few ppb. High positive biases are however seen over equatorial south America and Africa, which means that the simulations exceed the GOSAT observations here. In the surface+satellite inversion this bias vanishes; from Figure 7 it can be concluded that this is achieved by decreasing the wetland emissions in these regions. Another region where the surface-only inversion shows a significant bias with GOSAT is found south of the Himalaya plateau, where the a posteriori simulations underestimate the observations. Since these locations are adjacent to a region with many pixels rejected due to cloud contamination, this bias might also be present due to inaccuracies in the GOSAT data. Analysis of the GOSAT data removes the bias also here, but no clear change in emissions can be attributed to this. The lower panels show maps of rmse values, which provides similar information as the bias maps in this case. The rmse values are about 10 ppb at tropical and mid latitudes, and reach values up to 15 ppb at northern 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 14. The mean values in the observations clearly show the globally increasing trend in XCH4 in time. The values for 2018 are therefore the highest observed during this time period. This increasing trend is reproduced by both the surface-only inversions (left panel) and the surface+satellite inversion (right panel). The bias left after inversion of surface observations only is nearly zero for the earlier years, but are more negative (simulations underestimate observations) at higher latitudes and in the 2017-2018 period. These biases are to a large extend removed when the GOSAT data is used in the inversion too. The rmse (lower panels) values show similar features, with larges values at northern high latitudes, and for recent years occasionally at 25o-30o N (south of Himalaya). At these locations the uncertainty in retrievals is higher due to lower solar zenith angles and cloud contamination, and the associated weight in the inversion is therefore smaller.



Figure 13 – Comparison between GOSAT XCH4 observations (bias corrected for TM5-GOSAT differences), and reanalysis simulations over 2009-2018 from v18r1 (NOAA only, left column) or v18r1s (NOAA+GOSAT, right column). Top panel shows average observations, second row average simulations, third row bias between observations and simulations, and bottom row root-mean-square error. All statistics are computed over GOSAT pixels collected in 1o×1o grid cells.



Figure 14 - Zonal statistics over and between (TM5 bias corrected) GOSAT XCH4 observations and simulations, collected over latitude bands of 10 degrees and per month. Left column shows results for v18r1 (NOAA), right column for v18r1s (NOAA+GOSAT). Top row shows average observations, second row average simulations, third row bias, and bottom row root-mean-square error.



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 use the averaging kernels that are provided with the product into. 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 wiki1. 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 15. 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); these have been averaged over intervals of 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 Error! Reference source not found. which shows the number of observations available per month. 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 15 – Left panel: map with locations of TCCON sites used for current validation; colors indicate number of available XCH4 samples (temporal averaged). Right panel: total number of (1.5-hourly averaged) TCCON XCH4 observations available per month during the reanalysis period.

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



5.3 Timeseries of statistics The observed XCH4 columns have been compared with their simulated counterparts from the a posteriori simulations. Figure 16 shows time series of bias and rmse over all available data. The results for the surface-only inversion (v18r1, red) and surface+satellite inversion (v18r1s, green) are very similar for the years where they overlap (2009 onwards). The bias that is left after inversion is for most months positive, with values of about 5-10 ppb. An exception is formed by the years 2004-2005 which show an even more positive bias; these are however also years where only a few sites were present which limits the statistical robustness. The lower panel shows that the rmse values after inversion are about 10-20 ppb, with higher values for the earlier and recent years. Figure 16 - Bias (top) and rmse (bottom) between TCCON observed and simulated XCH4 columns per month during the reanalysis period.



5.4 Comparison by latitude and time Statistics for TCCON XCH4 columns have been computed per latitude band of 10 degrees and per month, and are shown in Figure 17. The left column shows results for the surface-only inversion, the right column for surface+satellite. The observations clearly show the increasing trend in CH4 concentrations: between 2005 and 2018, 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 overestimating the total columns at the northern hemisphere and underestimating 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 16. In the surface-only inversion (left panel), large positive biases are occasionally seen around 45oN, which corresponds to the stations in U.S. These high biases disappear for the 2015-2017 period when the GOSAT data is analyzed too (right panel). This indicates that, in spite of the need for a bias correction, inversion of GOSAT data provides useful information on the methane columns. The overall effect of the GOSAT data on bias and rmse is small, since the values remain dominated by the too-strong north-south gradient.



Figure 17 - 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 statistics for reanalysis results for v18r1 (NOAA only), right column for v18r1s (NOAA+GOSAT, from 2009 onwards). Second row shows average simulations, third row bias between observations and simulations, and bottom row root-mean-square error.



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 1990-2018), flight observations have been collected from the 7 collections shown in Figure

18. The second panel shows the number of observations available per year for each campaign. Only observations in the free troposphere were collected, here defined as altitudes above the boundary layer height in ECMWF meteorological data and below 10 km. For the CARIBIC observations that are taken on board of commercial aircrafts this lead to selection of only a small fraction of the total amount of observations available, which are mainly taken in the tropopause and lower stratosphere. The longest record of aircraft observations is available from NOAA, which has observations from 1992 onwards. The largest datasets are available from the HIPPO campaigns in 2010-2012 and the ATOM campaigns in 2016 and 2017, which both concern long series of measurements with a research aircraft.

6.2 KPI values The bottom panels of Figure 18 show the bias and standard deviation between the aircraft observations and a posteriori simulations with the TM5 model, for either the surface-only inversion (third panel) or the surface+satellite inversions (bottom panel). The statistics are computed per year. Given the large differences in location and number of observations per campaign, the values are computed for each data set individually. The limit values for the KPI are defined as 10 ppb for the absolute bias and 20 ppb for the standard deviation. The (absolute) biases are almost all below the target value of 10 ppb, with most values even below 5 ppb. For the surface-only inversion (3rd panel), highest biases are found for the Amazon campaign and for some years of CARIBIC data. For the surface+satellite inversion (bottom panel), highest biases are found the flights over Europe and from CARIBIC, where the later are just above 10 ppb. In general, the biases are more positive when the satellite observations are included in the inversion, which is especially visible for the research flights over Europe. The comparison shows quite different biases for the various campaigns. For example, where most biases are positive (model exceeds observations), the biases with the HIPPO campaign is negative for the surface-only inversion (model under estimates observations). The origin of this could be found in the large amount of observations in the southern hemisphere available from this campaign. As seen already in the comparison with TCCON observations, the TM5 simulations underestimate the total CH4 column in the southern hemisphere due to a latitudinal gradient that is too strong, and this is visible in the simulations for the free troposphere.



A notable difference between the surface-only and surface+satellite inversions is seen for the data of the Amazon campaign. As shown in Figure 13, the inversion including satellite data leads to a significant change in the methane column in the region where these flights were made. This has led to a strong reduction of the bias with the observations of the Amazon campaign. The computed standard deviations are for most campaigns and years below the target value of 20 ppb. In general, the air craft observations show strong variations on short time scales, which are difficult to represent by the model and could therefore lead to high standard deviations. Smallest standard deviations are obtained for the NOAA flights, although also these show values above 20 ppb for some years. The highest standard deviation of about 25 ppb is left for the flights over Europe in 2013 when also satellite data is analyzed. Table 3 shows a summary of the bias and standard deviation values computed over all years. When all available data is taken into account, the bias and standard deviation for the v18r1 release (surface only) are below the target values of 10 and 20 ppb respectively. For the v18r1s release (surface + satellite) the standard deviation slightly exceeds the thresholds. When only observations over the ocean are selected, thus omitting locations over land where the aircrafts sample emission plumes, the absolute bias and standard deviation are well below the target values. Table 3 – Summary of KPI values over time range of reanalysis, either over all locations or limited to samples over oceans only.

KPI’s on quality of CH4 inversion products

bias ± std.dev. all locations oceans only

v18r1 (NOAA only, 1990-2018) 2.3 ± 19.5 -1.1 ± 17.4

v18r1s (NOAA+GOSAT, 2009-2018) 5.8 ± 20.2 3.7 ± 17.5



Figure 18 – Comparison with aircraft observations in the free troposphere. Upper panel: locations of observations used in the comparison per campaign. Middle: number of observations per year. Bottom panels: bias and std.dev. between a posteriori simulations and observations collected per year.



7. Summary and outlook The inverted CH4 fluxes described in this report are part of the CAMS/GHG CH4 flux inversion 1990-2018. Fluxes are available from two inversion streams. The first stream uses only NOAA surface observations to optimize emission fluxes, and the second stream analyzes in addition GOSAT satellite observations too. The surface+satellite stream requires application of a bias correction on the GOSAT XCH4 column data, to compensate for a persistent bias between the 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. New information on the latitudinal bias will become available from comparison with TROPOMI observations, which is available from 2018 onwards and will be included in future validations and inversions. The current v18r1(s) releases have been created in a single production, in contrast to previous releases that combined output from different productions. As a result, the estimated fluxes and the validation results are rather stable over the entire time range. Comparisons have been made between the a posteriori simulations and observations. The results can be summarized as follows.

• The bias with NOAA surface observations is less than 5 ppb for the analyzed data (collected per month), and a rooted-mean-square (rmse) of 10-15 ppb. For surface observations that were not included in the inversions, for example because the sites are known to be difficult to represent by the model, the bias and rmse per month after inversion are higher (about 5-10 and 20-40 ppb respectively). For the surface+satellite inversion, the bias with the analyzed surface data is very comparable, except for first years of this stream (2009-2010) which shows a slightly higher bias. The initialization of the surface+satellite stream will be part of future study.

• The mean difference with the bias-corrected GOSAT XCH4 columns is less than 5 ppb when these observations are also analyzed. Largest biases remain at higher northern latitudes and south of the Himalaya plateau.

• The bias between total XCH4 columns and observations from the TCCON network (1.5 hourly averaged) is usually less than 10 ppb per month, except for the first years for which TCCON observations are available (2004-2005).

• The absolute bias between CH4 concentrations and aircraft observations in the free troposphere is for most flight campaigns less than 10 ppb over a year, and often even less than 5 ppb. The standard deviation in this comparison is about 20 ppb.

In the current release, the inversion system received a major update of the a priori emission inventories. The anthropogenic and rice emissions are now obtained from EDGAR v4.3, wetland emissions are obtained from an LPJ-wsl simulation, and for recent year biomass-burning emissions are obtained from GFAS. The a posteriori emissions are however very consistent with previous results. For the 1990’s, the inversion strongly increases the wetland, rice, and for some years also anthropogenic emissions, with a total sum of about 50 Tg/year. Since the new EDGAR inventory



prescribes lower emissions for this period, this increase is higher than in previous releases. Especially for the 1990’s the changes applied to the emissions show a more consistent picture than for the previous release. Events as the Pinatubo eruption in 1991, and strong burning events in Indonesia in 1997 are responsible for known variability in the observed methane concentrations, but these seem to a large extent captured by the new a priori inventories, even if these are not explicitly included such as the Pinatubo event. The new a priori inventories include more temporal variation, for example inter-annual variation in the wetlands and prescribed seasonal cycles in the anthropogenic emissions. The new a priori seasonal cycles are in general in better agreement with the results after inversion. The seasonal cycles after inversions are actually very consistent between the previous and current release. For example, independent of the a priori inventory the seasonal peak in wetland emissions is placed in July/August, while in the inventories it shifted from May to August. For the anthropogenic emissions the strongest change in the previous and current release is a reduction of emissions in China and India during the first months of the year. But since the new seasonal cycle in the EDGAR inventory prescribe rather high emissions for these months, the net result is still that emissions in winter are higher than in summer, while in the previous release it led to a reversed seasonal cycle with highest emissions in summer. From this once could conclude that changes to the total emissions are most dominant, and these describe whether in a certain season, emissions should be increased or decreased; the attribution of this to different categories is more sensitive to what is prescribed in the a priori inventories. A target of future research should also be to investigate whether the assumed temporal correlation in changes applied to the anthropogenic emissions are still valid now that a seasonal cycle has been introduced in the EDGAR inventory. When GOSAT observations are included in the inversion, the results are in general similar to the inversion using surface observations only, when looking at the total emission change. Since a bias correction is applied for the TM5-GOSAT differences, this is also expected. Notable differences are present however in the attribution to source categories in specific regions. The surface-only inversion shows for example a bias with the GOSAT columns over equatorial regions of South America and Africa. Inversion including the GOSAT data decreases the wetland emissions to remove this difference, which is also reflected in a low bias with aircraft data from the Amazon campaign. Also the comparison with TCCON columns shows that for locations in the U.S. the use of the GOSAT product is able to remove occasionally a difference with the observed XCH4 columns. The overall use of the satellite product remains however hampered by the need for a TM5-GOSAT bias correction, and this will therefore remain subject of future study. Another topic that needs to be addressed in the future is the balance between source and sink terms in the model. A free model run over the entire time period showed a negative growth rate in simulated methane concentrations for the 1990’s, while the observations show a steady increase over the full simulation period. This negative growth rate is even more visible now that the new EDGAR inventory subscribes lower emissions for the 1990’s. To correct this, the highest priority is assigned to an update of the atmospheric sink terms of OH, Cl, and O(1D) which are currently based on climatological data. Simulations out of the CAMS global model suite could be used to investigate the impact of inter-annual variability of the sink terms, and might become a source of input data for new releases.



8. References Bândă, N., Krol, M., Noije, T., Weele, M., Williams, J. E., Sager, P. Le, … Röckmann, T. (2014). The

effect of stratospheric sulfur from Mount Pinatubo on tropospheric oxidizing capacity and methane. Journal of Geophysical Research: Atmospheres, 120(3), 1202–1220. https://doi.org/10.1002/2014JD022137

Bândă, N., Krol, M., van Weele, M., van Noije, T., Le Sager, P., & Röckmann, T. (2016). Can we explain the observed methane variability after the Mount Pinatubo eruption? Atmospheric Chemistry and Physics, 16(1), 195–214. https://doi.org/10.5194/acp-16-195-2016

Bloom, A. A., Bowman, K. W., Lee, M., Turner, A. J., Schroeder, R., Worden, J. R., … Jacob, D. J. (2017). A global wetland methane emissions and uncertainty dataset for atmospheric chemical transport models (WetCHARTs version 1.0). Geoscientific Model Development, 10(6), 2141–2156. https://doi.org/10.5194/gmd-10-2141-2017

Reshetnikov, A. I., Paramonova, N. N., & Shashkov, A. A. (2000). An evaluation of historical methane emissions from the Soviet gas industry. Journal of Geophysical Research: Atmospheres, 105(D3), 3517–3529. https://doi.org/10.1029/1999JD900761

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

Zhang, Zhen, Niklaus E Zimmermann, Leonardo Calle, George Hurtt, Abhishek Chatterjee, and Benjamin Poulter (2018). Enhanced Response of Global Wetland Methane Emissions to the 2015-2016 El Nino-Southern Oscillation Event. Environmental Research Letters 13 (5), 074009. doi: 10.1088/1748-9326/aac939


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