Estimation of atmospheric methane emissions between 1996 and 2001 using a three‐dimensional global chemical transport model

Abstract

[1] Using an atmospheric inversion approach, we estimate methane surface emissions for different methane regional sources between 1996 and 2001. Data from 13 high-frequency and 79 low-frequency CH 4 observing sites have been averaged into monthly mean values with associated errors arising from instrumental precision, mismatch error, and sampling frequency. Simulated methane mole fractions are generated using the 3-D global chemical transport model (MATCH), driven by NCEP analyzed observed meteorology (T62 resolution), which accounts for the impact of synoptic and interannually varying transport on methane observations. We adapted the Kalman filter to optimally estimate methane flux magnitudes and uncertainties from seven seasonally varying (monthly varying flux) and two aseasonal sources (constant flux). We further tested the sensitivity of the inversion to different observing sites, filtered versus unfiltered observations, different model sampling strategies, and alternative emitting regions. Over the 1996-2001 period the inversion reduces energy emissions and increases rice and biomass burning emissions relative to the a priori emissions. The global seasonal emission peak is shifted from August to July because of increased rice and wetland emissions from southeast Asia. The inversion also attributes the large 1998 increase in atmospheric CH 4 to global wetland emissions. The current CH 4 observational network can significantly constrain northern emitting regions but not tropical emitting regions. Better estimates of global OH fluctuations are also necessary to fully describe interannual methane observations. This is evident in the inability of the optimized emissions to fully reproduce the observations at Samoa.

Estimating wetland methane emissions from the northern high latitudes from 1990 to 2009 using artificial neural networks

“…WTD stands out as the most sensitive and consistent factor, suggesting that the water Matthews and Fung [1987] 62 Estimates for forested and nonforested bogs between 50 N and 70 N Crill et al [1988] 72 Estimates for undrained peatlands north of 40 N Whalen and Reeburgh [1990] 53 Estimates for global tundra and taiga ecosystems Fung et al [1991] 35 Estimates for wetlands and tundra Whalen and Reeburgh [1992] 42 AE 26 Estimates for Arctic wet meadow and tussock and shrub tundra Bartlett and Harriss [1993] 38 Estimates for northern wetlands north of 45 N Cao et al [1996] 22 Estimates for natural wetlands north of 40 N Christensen et al [1996] 20 AE 13 Estimates for northern wetlands and tundra Walter et al [2001] 65 Estimates for wetlands north of 30 N Zhuang et al [2004] 57 Estimates for natural wetlands north of 45 N Mikaloff Fletcher et al [2004] 30-64 Estimates based on inverse modeling for boreal region Chen and Prinn [2006] 34 AE 13 Estimates based on inverse modeling for boreal region wetlands Petrescu et al [2010] 38 [Petrescu et al, 2010;Walter et al, 2001]. This simple representation of WTD neglects the effects of microtopography on water table dynamics, to which CH 4 production and oxidation processes are sensitive [Zhuang et al, 2007].…”

The global methane budget 2000–2012

“…The geographical distribution of the emissions is assessed by global (USEPA, 2006(USEPA, , 2012EDGARv4.2FT2010, 2013 and regional Chen et al, 2013;Chen and Prinn, 2006;Yan et al, 2009;Castelán-Ortega et al, 2014; inventories or by land surface models (Spahni et al, 2011;Zhang and Chen, 2014;Ren et al, 2011;Tian et al, 2010Tian et al, , 2011Li et al, 2005;Pathak et al, 2005). The emissions show a seasonal cycle, peaking in the summer months in the extratropics associated with the monsoon and land management.…”

“…Therefore, the optimized fluxes x a are obtained using classical minimization algorithms (Chevallier et al, 2005;Meirink et al, 2008b). Alternatively, Chen and Prinn (2006) computed monthly emissions by applying a recursive Kalman filter in which P a is computed explicitly for each month. Emissions are generally derived at weekly to monthly timescales, and for spatial resolutions ranging from Table 3.…”

Atmospheric constraints on 2004 emissions of methane and nitrous oxide in North America from atmospheric measurements and a receptor-oriented modeling framework

“…Many global-scale studies have investigated emissions of methane and nitrous oxide (Prinn et al 1990;Chen and Prinn 2006;Hirsch et al 2006;Houweling et al 2006;Bergamaschi et al 2007;Nevison et al 2007;Huang et al 2008). These studies help immensely to improve understanding of emissions at large spatial and temporal scales (continental and annual).…”