Atmospheric Methane Evolution the Last 40 Years

Australia: The Land Where Time Began

Atmospheric Methane Evolution the Last 40 Years

An increase in global mean surface methane (CH₄) of about 180 parts per billion (ppb) (above 10%) of the period 1984-2012 has been observed at surface sites. Large fluctuations in the annual growth rate have been recorded over this period of time. In this paper Dalsøren et al. report their investigations of the atmospheric CH₄ evolution over the period 1970-2012 with the Oslo CTM3 global chemical transport model (CTM) in a bottom-up approach. They assessed thoroughly data derived from surface measurement sites in international networks and selected a subset that was suited for comparisons with the output from CTM. According to Dalsøren et al. they compared model results and observations in order to understand the causes of both long-term trends and short-term variations. They were able to reproduce the seasonal and year-to-year variations and shifts between years with consecutive growth and stagnation, at a global as well as regional scale by using Oslo CTM3. Over the period the overall CH₄ trend is reproduced though the model does not reproduce the strength of the growth for some periods. The growth that is observed after 2006 is overestimated by the model in all regions. This appears to be explained by an increase in anthropogenic emissions in Asia that is overly strong, having a global impact. Other studies that question the timing or strength of the changes in emission in Asia in the EDGAR v4.2 emission inventory over recent decades are questioned by the findings of this study. Changes in sources not only control the evolution of CH₄, but also changes in the chemical loss in the atmosphere and uptake by the soil. The lifetime of atmospheric CH₄ is an indicator of the loss of CH₄. The lifetime of atmospheric CH₄ decreases by more than 8 % from 1970-2012 in the simulations in this study, which is a significant reduction of residence time of this important greenhouse gas. Most of this is driven by changes in CO and NO_x emissions, specific humidity, and ozone column, and Dalsøren et al. provide simple prognostic equations for the relations between those and the lifetime of the CH₄. Substantial growth in the chemical CH₄ loss, relative to its burden, and dampens the growth of CH₄.

Over the industrial era the abundance of atmospheric CH₄ more than doubled. The irradiative forcing that resulted is second after CO₂ in terms of atmospheric forcing from greenhouse gases (Myhre et al., 2013). A high degree of uncertainty has remained regarding the contributions from

i) specific sectors of source and regions to the CH₄ emissions (Neef et al., 2010; Kirschke et al., 2013; Houweling et al., 2014; Melton et al., 2014; Melton et al., 2013; Bruhwiler et al., 2014: Schwietzke et al., 2014; Bridgham et al., 2013: Pison et al., 2009; Ciais et al., 2013),

ii) The underlying factors that contribute to trends that are observed (Dlugokencky et al., 2009, 2003; Wang et al., 2004;Kai et al., 2011; Aydin et al., 2011; Simpson et al., 2012; Bousquet et al., 2006, 2011; Pison et al., 2013; Bergamaschi et al., 2013; Monteil et al., 2011; Ghosh et al., 2015; Nisbet et. al., 2014; Fiore et al., 2006; Levin et al., 2012), and

iii) In feedbacks from the biosphere and permafrost (Bridgham et al., 2013; Melton et al., 2013; Isaksen et al., 2011; O’Connor et al., 2010).

The uncertainty in the understanding of current budgets, recent trends and feedbacks limit the degree of confidence in the accuracy projecting into the future evolution of CH₄. Near-term warming, as a result of its strong impact on climate on a 20 year time frame (Myhre et al., 2013) would be accelerated by increasing atmospheric CH₄. The levels of ozone in the surface air would also be increased by enhanced levels of CH₄ (Fiore et al., 2008, 2012; West & Fiore, 2005; Isaksen et al., 2014), and thereby worsen the effects of air pollution on vegetation, crops, and human health.

This study aimed to increase understanding of CH₄ by providing a detailed analysis on global and regional evolution of CH₄ over the last 40 years. Essential natural and anthropogenic drivers that control the atmospheric CH₄ budget over the period, was investigated by this study, with a particular focus on the last 15 years. The sinks are dependent on the capacity of the atmosphere for oxidation, which is determined by complex chemical and meteorological interactions. This study attempts to reveal the key chemical components and meteorological factors which affect recent changes in the oxidation capacity. This study compared model studies and observations in order to understand causes for both long-term trends and short-term variations (year-to-year). Reasons for differences between CH₄ trends were also addressed by this study. The study used the methods that are described in Sect. 2. The results from the main analysis of this study are presented in sect. 3 and they were discussed in a broader context related to findings from other studies. The supplement presented additional sensitivity studies. The findings of this study are summarised in Sect. 4.

Summary and conclusions

It has been made hard for bottom-up and well as top-down studies to settle the global CH₄ budget, untangle the causes for recent trends and predict evolution of CH₄ in the future, by uncertainties in physical and chemical processes in models, data that is input on emissions and meteorology, and limited spatial and temporal coverage of measurement data (Ciais et al., 2013; Kirschke et al., 2013; Nisbet et al., 2014). The chances of understanding more pieces in the big puzzle increase as the level of quality and detail of models, input data, and measurement data increase. According to Dalsøren et al. this study is an effort in such a perspective.

In the bottom-up approach of this model, a global chemical transport model (CTM) was used in order to study the evolution of atmospheric CH₄ over the period 1970-2012. A thorough comparison with CH₄ measurements from surface stations that cover all the regions of the Earth is included in this study. At most stations the seasonal variations are reproduced. The observed evolution of CH₄ on interannual as well as decadal time scales was reproduced in the model. Major drivers for year-to-year variation of CH₄ are variations in wetland emissions. With regard to trends, the causes have been much debated, as has been discussed in previous sections. There is no consensus on the relative contributions from individual emission sectors, or the share of natural vs. anthropogenic sources. The fact the much of the regional changes that have been observed is captured in the simulations of this study indicates that the transport and chemistry schemes of Dalsøren et al. perform well and that emission inventories that were applied are reasonable with regard to temporal, spatial, sectoral, and natural vs. anthropogenic distribution of emissions. There are, however, some large discrepancies in the performance of the model which questions the accuracy of the CH₄ emission data in certain regions and periods. For recent years potential flaws have been pinpointed when the model simulations are more complete with regard to input data (e.g. emissions, variable meteorology etc.) and more measurements are available for comparison. Following a period from 2000 to 2006, which was a period of stable CH₄ levels, increasing levels are shown from 2006 in both hemispheres. Beginning in 2006, the model overestimates the growth in all regions, in Asia in particular. In most regions of the world the CH₄ trends are influenced by large growth of emissions in Asia. Other studies are supported by the findings of this study that suggest that recent growth in anthropogenic emissions in Asia is too high in the EDGAR v4.2 inventory. Dalsøren et al. say they also question the emission trends in Asia in the 1990s and the beginning of the 2000s in the EDGAR v4.2 inventory, based on the results from their model and comparison between ECLIPSE and EDGAR v4.2 emissions, Though the limited number of measurement sites in Asia makes it difficult to validate this.

The evolution of CH₄ that is modelled also depends on changes in the atmospheric CH₄ loss. The lifetime of the atmospheric CH₄ is an indicator of the loss of CH₄. In the simulations of Dalsøren et al., the lifetime of CH₄ decreases by more than 8% from 1970 to 2012. Increased capacity of the atmosphere for oxidation is the reason for the large change. In theory, such large changes are driven by complex interactions between a number of chemical components and meteorological factors. The analysis of Dalsøren et al. reveals, however, that changes in specific humidity, NO_x/CO emission ratio, and total ozone column, are key factors for the development. Predicting the lifetime of CH₄ by a combination of these parameters in a simple equation is statistically valid. The change that is calculated in the lifetime of atmospheric CH₄ is within the range that is reported by most other bottom-up models. Findings from these studies do not, however, agree fully with top-down approaches that use observations of CH₃CCL₃ or ¹⁴CO.

The growth in CH₄ over the last decades would have been much higher without the calculated increase in oxidation capacity. It is also likely that the loss of CH₄ contributed to the stagnation of the CH₄ growth between 2001 and 2006. Over the last few years, however, the loss has deviated from its steady increase over the previous decades. It appears that much of this deviation is caused by variation in meteorology. It was revealed by simulations in this study that the accounting for variation in meteorology has a strong effect on the loss of CH₄. In turn, this affects inter-annual and long-term changes in CH₄ burden. A stabilisation of loss of CH₄, which resulted mainly from meteorological variability, is likely to have contributed to a continuing increase (2009-2012) in CH₄ burden following high emission years in 2007 and 2008. This could also contribute to growth of CH₄ in the future due to the long response time. There are extra uncertainties in the results of the model, however, after 2009 that are the result of a lack of comprehensive emission inventories. It would have been a very valuable piece for model studies that try to close the gaps in the CH₄ puzzle if there was a new inventory or update of existing ones with sector[-vice?] separation of emission for recent years (2009-2015). Dalsøren et al. suggest it will also provide important fundament for predictions that are more accurate of future CH₄levels and various mitigation studies.