7. Modeling of atmospheric methane using inverse (and forward) approaches
7.2 Inverse modeling approaches for understanding Arctic methane emissions
7.2.4 Results from inverse model studies
As part of the present assessment, ten atmospheric inverse models were reviewed and robust features in the results that are common among them were identified. Results from six of the models were compiled by Kirschke et al. (2013) in a synthesis study of the global methane budget over recent decades. The present study has built on this work by obtaining more recent results from the studies of Bergamaschi et al. (2013) and Houweling et al. (2014).
The inverse model approaches are summarized in the Appendix.
The various inverse model results span a range of possible configurations and assimilation techniques. Several different transport models and driving meteorological data products are used, thus allowing for evaluation of possible transport biases. The spatial resolutions of the transport models range from 3.75°×2.5° to 6°×4°, with the number of vertical levels ranging from 19 to 47. Multiple optimization techniques are used and fall into the categories of variational and ensemble
approaches. Some of the inverse models use only surface observations while others use a combination of space-based and surface observations. The spatial resolutions of the emissions estimated by the inverse models span the transport model grid scale (i.e. 6×4 grid boxes) up to continental-scale source regions. In practice, this means that some inverse models will solve for emissions coming from each Arctic transport model grid box while others will solve for the net emissions spread over regions the size of Siberia or the North American Arctic.
The frequency at which emissions are estimated by the inverse models considered here is either monthly or weekly.
As previously noted, inverse model results are dependent on prior emission estimates. All of the inverse models compiled here use the widely available anthropogenic emissions inventories EDGARv3.2 or EDGARv4.1 (Emission Database for Global Atmospheric Research; European Commission 2009) for prior anthropogenic emissions estimates. These products cover the past few decades. Prior estimates of biomass burning emissions come from either GFEDv2 or GFEDv3 (Global Fire Emissions Database; Giglio et al. 2006; van der Werf et al. 2006).
For wetland emissions, the models used either Matthews (1989) or the Kaplan (2002) wetland distribution and parameterization based on soil carbon, moisture and temperature. None of the inverse approaches included in this study used detailed bottom-up wetland process models to provide prior estimates of wetland emissions.
The inverse models considered also vary in their approach to data selection. Observations from background atmospheric sites are universally used, however in some cases only sites with long data records were used. Other inverse models used harder-to-model continental sites in addition to background sites, or retrievals derived from radiances observed from satellites. It is beyond the scope of this study to review in detail the sites used in each inverse model exercise, or the amount of weighting applied to the various observations used.
Box 7.2 The potential for satellite data to constrain atmospheric inverse models Surface observations maintained consistently for decades
provide the best means of detecting atmospheric methane trends and characterizing its global-scale distribution. They are also necessary for developing and evaluating remotely-sensed retrievals, such as those from satellites or ground-based, open path spectrometers such as TCCON. Space-based retrievals, on the other hand, have the advantage of frequent global coverage.
Using surface observations and satellite data together offers a reasonable approach to improving the ability of inverse models to reduce uncertainties in the budget of atmospheric trace gases.
The calibration and validation of current satellite observations for methane is an ongoing endeavor, particularly in linking them to the ground surface measurements and calibration of the WMO-GAW (Global Atmosphere Watch program of the World Meteorological Organization) greenhouse gases program which is essential to ensure global consistency across horizontal and vertical dimensions. A particular concern is possible drift over time, and consistency between satellite data records. Current satellite instruments have been shown to have persistent biases in space and time (e.g. Bergamaschi et al. 2013; Houweling et al. 2014) that must be accounted for
if satellite data are to be assimilated into atmospheric inverse models. Remotely-sensed observations of column methane using ground-based upward looking Fourier spectrometry have been used to detect biases in the satellite data and this has resulted in bias correction schemes that have been somewhat successful (Houweling 2014).
The current satellite instruments provide only limited information for Arctic regions. Instruments operating in the visible and short-wave infrared spectrum, such as SCIAMACHY and GOSAT, rely on sunlight, which is absent during the Arctic winter. In other months of the year, the low angle of the sun complicates the retrieval of information from satellite radiance data. Infrared sounders, such as AIRS and IASI, are mostly sensitive to the upper troposphere, where signals of surface emissions are small. At higher latitudes, their sensitivity is further reduced by the lack of thermal contrast between the surface and the atmosphere as well as uncertainties in surface emissivity related to variations in snow and ice cover. Mission plans are emerging that will improve polar region coverage and the measurement instrumentation for methane, but are some years from implementation.
7.2.4.1
Inverse model estimates of source magnitude
As shown in Table 7.1, the atmospheric inverse model results surveyed agree to within ~40% for total Arctic methane emissions for the years 2000 to 2010 (the period over which the maximum number of model results is available). The average annual total emission across ten inverse models is 25 Tg CH4/y with a wide range spanning 18.5 to 28.8 Tg/y. The largest contribution to Arctic emissions is from wetlands, followed by anthropogenic emissions. There is a small but interannually variable contribution from biomass burning.
While the inverse model studies show relatively good agreement among them, all tend to reduce estimates of high latitude emissions relative to priors, implying that the prior emissions are too large and inconsistent with observed methane levels in the atmosphere. The estimates of McGuire et al. (2012) for 2000–
2010 based on pan-Arctic terrestrial flux measurements suggest a source of 25.0 Tg CH4/y from Arctic tundra wetlands with uncertainty ranging from 10.7 to 38.7 Tg CH4/y. Atmospheric inverse models suggest a lower source of 15.5 Tg CH4/y on average from the region 60–90°N over the period 2000–2010 (see Table 7.1). Estimates from field studies may be biased towards larger emissions if measurement sites tend to be located near large sources and do not represent the Arctic over large scales.
This could at least partially account for the lower estimates based on atmospheric observations. On the other hand, the inverse models may not be able to accurately distinguish between anthropogenic and natural emissions. If the anthropogenic emissions are overestimated, then the estimated emissions from wetlands could be larger resulting in better agreement with the bottom-up estimates. Furthermore, as discussed in Sect. 7.2.2, if the models are biased towards stability then emissions could be underestimated. However, it is very encouraging that the bottom-up and top-down approaches are reasonably consistent.
In addition to wetlands, other significant natural methane emissions have recently been proposed for the high northern latitudes. Walter Anthony et al. (2007) estimated that ebullition (i.e. direct release of methane bubbles) from Arctic lakes could add an additional 24±10 Tg CH4/y, an estimate on a par with bottom-up estimates of wetland emissions. Relatively shallow lake waters enable bubbles to transport methane directly and rapidly to the atmosphere from sources such as buried methane hydrates and organic-rich, anoxic sediments. Shakhova et al.
(2014) estimated a methane hydrate source of ~17 Tg CH4/y for the shallow continental shelf waters of the Eastern Siberian Arctic Shelf (see Ch. 4). Walter Anthony et al. (2012) proposed that seepage of methane from geologic sources may also occur on land as permafrost thaws and glaciers recede even though hydrates require high pressure and low temperature to exist (meaning they must lie far below the surface). Total natural emissions including all of these processes would be over 70 Tg CH4/y, an amount that significantly exceeds the total Arctic emissions (i.e. including anthropogenic emissions) as estimated by the inverse model studies constrained by atmospheric observations (Table 7.1). Note that many of the bottom-up studies rely on a small number of observations that are extrapolated to pan-Arctic annual total emissions.
A number of factors may be contributing to the discrepancy between the bottom-up estimates and top-down atmospheric
inverse model results. It is possible that the polar atmosphere in atmospheric transport models is too stable, leading to a simulated accumulation of methane near the surface (rather than mixing and diluting methane throughout the atmospheric column). The inverse model will therefore reduce emissions in order to match observations. Recent studies have addressed the potential for transport errors to be aliased into estimated emissions (Patra et al. 2011; Locatelli et al. 2013). There could also be some double counting of emissions in bottom-up estimates between natural wetlands and other inland water areas, as well as incorrect extrapolation of local emissions to pan-Arctic scales. However, the spatial and temporal information coming from the observations ultimately places strong constraints on the amount of methane that can be emitted in the Arctic and elsewhere according to inverse models.
It is clear that the total amount of all the proposed emissions from wetlands, lakes, possible geologic sources and the shallow Eastern Siberian Arctic Ocean, together with the Arctic anthropogenic emissions, is significantly larger than the total emissions implied by atmospheric observations. This suggests that either some bottom-up emissions are overestimated, or that the loss processes that remove methane from the atmosphere are not yet well understood. Note, however, that quantifying and correcting possible model transport biases may result in higher emissions estimates.
7.2.4.2
Inverse model estimates of spatial variability in methane emissions
The global latitudinal distribution of total methane emissions estimated by the inverse models is shown in Fig. 7.2. From this graphic, it is evident that the tropics and populated sub-tropical latitudes dominate the global methane budget. The spread between the results is considerable, although all models show large tropical and northern sub-tropical emissions. There do not appear to be systematic differences between those approaches that use space-based observations and those that do not. Likewise, properties such as which transport model or assimilation technique is used do not appear to stand out (see Appendix).
Figure 7.2 also shows the zonal distribution of estimated emissions for Arctic latitudes. With the exception of one inverse model that appears too high relative to the others, the models agree to within about 40% for each 1° latitude zone.
All models except one show a steep decline in emissions with increasing latitude.
Table 7.1 Average annual emissions for the period 2000–2010 from multiple inverse model studies for the Arctic region (60° to 90°N). Three inverse model studies calculated total emissions only, so the total emissions were averaged across ten studies. Seven inverse model studies were used to compute the averages for each source category. See the Appendix for details about the inverse model studies. Source: Bergamaschi et al. (2013), Kirschke et al. (2013), Houweling et al. (2014).
Source Tg CH4/y
Wetlands 15.5 (11.1–27.4)
Biomass burning 0.6 (0.4–1.0)
Anthropogenic 9.3 (7.2–10.5)
Total emissions 25.0 (18.5–28.8)
7.2.4.3
Inverse model estimates of temporal variability in methane emissions
Monthly total methane Arctic emission estimates from the inverse model studies over the past decade are shown in Fig. 7.3. Th e past decade was chosen because although some of the studies cover shorter periods (see Table 7.1) they all give results for at least some part of the period 2000–2010.
Note the large seasonal cycle of total emissions, with a peak during summer when microbial methane production occurs most rapidly due to seasonally warmer surface soils as well as an abundance of soil moisture. Th e winter minimum mostly refl ects anthropogenic emissions (because natural emissions are low in the cold season). Many of the inverse models do not provide error estimates; however, those for the CT-CH4 inverse model (light blue shaded area) have been included to show at least one estimated uncertainty range. Th e diff erences between the model results are oft en greater than the estimated error for the CT-CH4 data.
Th e lower panel of Fig. 7.3 shows the mean estimated Arctic emissions across the models, together with the model spread (i.e. the area between the highest and lowest model estimates, shaded area). Th e model spread can be large during summer,
sometimes up to 40 Tg CH4/y. During winter the spread is smaller; 20 Tg CH4/y or less. Th e mean for peak summer emissions is steady at about 55–60 Tg CH4/y.
Although the mean exhibits little interannual variability, some of the model results vary signifi cantly from year to year. For example, the CT-CH4, H_SCIA and H_GOSAT models show peak emissions of about 80 Tg CH4/y for 2007, although the Pi and Fr models show little diff erence in 2007. Results for 2008 are more variable with some models generating higher than average results and some lower.
None of the models show evidence of a trend over the period 2000–2010 towards increasing summer Arctic emissions.
Th is may indicate no trend, or that the inverse models are not sensitive enough to detect changes that have occurred.
Increasing observational coverage and ensuring the continuation of long records is essential for increasing the sensitivity of atmospheric inverse models to changes in emissions. It is also useful to develop, evaluate and improve bottom-up models of emissions so that sparsely observed regions are represented as well as possible, because biases and errors in the prior emissions coming from bottom-up models can end up biasing large-scale emission estimates produced by atmospheric inversion modeling approaches.
B_K B_NOAA B_ref
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Fig. 7.3 Distribution of monthly Arctic methane emissions for ten inverse models for the period 2000 to 2010 (upper panel). See Appendix for details of the individual approaches. Th e light blue shading represents the estimated 1 standard deviation confi dence interval (estimated error) for the CT-CH4 model. Th e mean (black line) and model spread (shaded blue) of the suite of model results is shown in the lower panel. Note that the model spread does not include the Pi model, which appears to be an outlier. Also note the diff erence in scale for the upper and lower panels.
B_K B_NOAA B_ref Pi Fr CT-CH4 Bg H_NOAA H_SCIA H_GOSAT Tg CH4/y
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Fig. 7.2 Annual average latitudinal (upper) and Arctic zonal (lower) distribution of methane emissions for ten atmospheric inverse model studies for the period 2000 to 2010. See Appendix for details of the individual approaches.
Th e average annual cycle of Arctic methane emissions estimated by the suite of models is shown in Fig. 7.4. Most inverse models show emissions below 20 Tg CH4/y during the cold season rising to values of 50–60 Tg CH4/y during the warm season when methane production in Arctic wetlands is highest. It is interesting that the models vary in the timing of their summer maxima. Two models have maximum summer emissions during June, while others have maxima in July or August. Th ere is no clear correlation between the timing of maximum emissions and whether satellite or surface observations are used to constrain the model. Note that the observed annual cycle in Arctic methane concentrations shows a minimum at mid-summer, when wetland emissions are highest (see Fig. 6.9), and a maximum during winter. Th e chemical loss of methane (due mostly to reaction with OH) is greatest during summer, when solar irradiance is highest and temperatures are warm, and the methane annual cycle results from this. A small secondary peak in Arctic methane concentrations can oft en be seen in observations during late summer and early autumn, and this is probably because wetland emissions are highest towards the end of the growing season when wetland soils are warmest, while at the same time chemical loss is slowing as the days grow shorter (see Ch. 6). Th is suggests that inverse models that show the greatest emissions aft er the summer solstice may be more realistic than those that do not.
Interannual variability in estimated emissions is further explored in Fig. 7.5. Interannual variability was computed by subtracting an average seasonal cycle (shown in Fig. 7.4) from the results of each model. Th e lower panel of Fig. 7.5 shows the mean and model spread of the interannual variability from the suite of inverse models. Th e spread is relatively high indicating that the models do not agree on the timing of emission anomalies (i.e.
when higher/lower than average seasonal emissions occur).
On the other hand, most models do agree that 2007 was a year with higher than average emissions, and this makes it possible to assess both the sensitivity of the models to variability in emissions and their ability to detect emission trends, since the climatic conditions for 2007 were exceptionally warm and wet (Dlugokencky et al. 2009). Th e models on average estimate that 2.2 Tg CH4 more than average (15.5 Tg/y, Table 7.1) were emitted across the Arctic during the warm season of 2007, with a spread of -0.4 to 5.2 Tg CH4.
Attribution of interannual variability in observed methane concentration to individual sources and regions is an important analysis contribution of the top-down approach. Based on zonal average analysis of atmospheric network observations, Dlugokencky et al. (2009) pointed out that in 2007 the global increase in methane was equal to about a 23 Tg imbalance between sources and sinks and that the largest increases in atmospheric methane concentration growth occurred in the Arctic (>15 ppb/y). Th is does not necessarily imply that the largest surface fl ux anomalies occurred at high northern latitudes. Bousquet et al. (2011) noted that the relatively weak vertical mixing characteristic of polar latitudes results in a greater response in atmospheric methane concentrations to anomalous surface emissions than at tropical latitudes where strong vertical mixing rapidly loft s methane emitted at the surface through a deep atmospheric column. Transport models used as a component of inverse models are in theory able to simulate the more stable polar atmosphere, and can therefore play an important role in helping to resolve surface fl ux signals from variability in atmospheric transport processes, although care must always be taken to consider possible biases in modeled transport.
Fig. 7.4 Average seasonal cycle of Arctic methane emissions for eleven inverse model studies for the period 2000 to 2010. See Appendix for details of the individual approaches. Th e shaded area is the estimated uncertainty for the CT-CH4 model.
2000 2002 2004 2006 2008 2010
2000 2002 2004 2006 2008 2010
B_K
Fig. 7.6 Interannual variability of Arctic methane emissions for ten inverse model studies computed by subtracting an average seasonal cycle for each inverse model (upper panel). See Appendix for details of the individual approaches. Th e mean (black line) and range (shaded blue) of the interannual variability for the suite of inverse model results is shown in the lower panel. Th e range is defi ned by the maximum and minimum of the inverse model ensemble.
7.3