Connecting atmospheric composition
with climate variability and change
Seminar in Atmospheric Science, EESC G9910
9/19/12 Observed methane trends in recent decades:
Emission trends or climate variability?
1. Aydin et al., Nature, 2011 (fossil fuel)
Study period: 20th century; ethane:methane in firn air
2. Kai et al., Nature, 2011 (NH microbial sources)
study period: 1984-2005; isotopic source signature
3. Hodson et al., GRL, 2011 (ENSO and wetlands)
study period: 1950-2005; process modeling
GMD monitoring network
http://www.esrl.noaa.gov/gmd/dv/site/map1.php
The Methane Mystery: Leveling Off then Rebounding
http://www.esrl.noaa.gov/gmd/aggi/
The uptick: observational
evidence suggests natural
sources in 2007 and 2008:
2007 Arctic
depleted in 13C (wetlands)
 Warm Arctic Temp
2008 tropics
(zero growth rate in
Arctic)
 La Nina, tropical precip
Dlugokencky et al., GRL, 2009
 Help characterizing sources from isotopes + co-emitted species
 Inverse constraints on sinks (confidence?)
[Montzka et al., 2011]
The Methane Mystery: Leveling Off then Rebounding
Heimann, Science, 2011, “news and views”
Possible sources of variability/trends in recent decades
SOURCES:
1. Wetlands: At present 2/3 tropics, 1/3 boreal;
estimated at 170-210 Tg CH4 (ENSO-driven; Hodson et al.)
-- T and water table (seasonal, interannual)
2. Biomass burning
3. clathrate/permafrost degassing
4. fossil fuel (also landfills/waste management) Aydin et al.
5. rice agriculture Kai et al. (+ wetlands – they can distinguish “microbial”)
6. ruminants
SINKS:
Atmospheric Oxidation (primarily lower tropical troposphere)
-- feeds back on any source change
-- amplified by changes in biogenic VOC (but chemistry uncertain!)
-- photolysis rates (e.g., due to overhead O3 columns; affects OH source)
-- water vapor (affects OH source)
-- shift in magnitude / location of NOx emissions (OH source)
Aydin et al., Nature, 2011
 Use Ethane as a proxy for fossil fuel methane
 2nd most abundant constituent in natural gas
 Released mainly during
production+distribution (same as CH4)
 Major loss by OH, ~2 month lifetime
METHODS:
1) Firn air measurements (flasks) at 3 sites:
Summit, South Pole, WAIS-D, analyzed with GC-MS
2) Derive annual mean high latitude tropospheric
abundances of ethane (1-D firn-air model + synthesis
inversion)
3) Explore role of biomass burning + fossil fuel in
contributing to observed ethane time histories (2-box
model, informed by 3-D model)
contemporary
modeled
Ethane mixing ratios in firn air
at three sites, and the
Atmospheric histories derived
from these measurements.
M Aydin et al. Nature 476, 198-201 (2011)
doi:10.1038/nature10352
Shaded regions not constrained
Due to uncertainties in PI levels
S Pole can constrain ramp-up
Starting 1910  5x by 1980
All 3 site show 1980 peak, then
decline (~10%) despite increase
in FF use
Not used
in
inversion
Possible atmospheric
histories
(different PI ethane)
Ethane source emissions and
the resulting atmospheric
histories.
 Derived with 2-box model
 3D model used to relate
how air reaching firn
responds to changing
hemispheric mean ethane
levels
1. FF dominates observed time history
2. Decline of CH4 growth rate parallels
ethane decline
3. Now steady  recent “uptick” not
due to FF CH4
M Aydin et al. Nature 476, 198-201 (2011) doi:10.1038/nature10352
Ethane and methane emissions from fossil fuels,
biofuels and biomass burning.
1. FF ethane differs from bottom-up CH4
2. BB agrees with independent estimates
Are the CH4 inventories wrong?
Could methane-to-ethane
Emission ratios have changed?
Less venting while production increased?
15-30 Tg CH4 yr-1 decrease 1980 to 2000
Shift in distribution / Cl sink estimated to
be small
M Aydin et al. Nature 476, 198-201 (2011)
doi:10.1038/nature10352
Kai et al., Nature, 2011
Use CH4 abundance plus 13C/12C of CH4 to distinguish microbial vs. fossil
sources, also distinguish sinks by looking at D/H
 information in inter-hemispheric difference (IHD)
Conclusion: Isotopic constraints exclude reductions in fossil fuel as
primary cause of slowdown. Rather, large role for Asian rice agriculture
(+fertilizer, -water use
METHODS:
1) measurements from UCI, NIWA, and SIL networks
2) Examine various hypotheses for explaining decline in CH4 growth
rate (2-box model including CH4 and its isotopes)
3) Empirical, process-based biogeochemical model to estimate changes
in rice agriculture
Kai et al., Nature, 2011
Kai et al., Nature, 2011
Kai et al., Nature, 2011
Long-term trends in atmospheric CH4, 13C-CH4, and D-CH4.
FM Kai et al. Nature 476, 194-197 (2011) doi:10.1038/nature10259
Long-term trends in atmospheric CH4, 13C-CH4, and D-CH4.
FM Kai et al. Nature 476, 194-197 (2011) doi:10.1038/nature10259
FM Kai et al. Nature 476, 194-197 (2011) doi:10.1038/nature10259
Possible driving factors of trend towards NH enriched C isotopes of CH4
1. Decrease in isotopically depleted source
(microbial: agriculture, landfills, wetlands)
2. Increase in enriched source (FF or BB)
1. Increase in removal by OH (but D relatively
constant  suggests no change in sink)
Considering CH4 alone, leveling off can
be explained by both FF and agricultural emissions
but isotopic time histories differ for FF / agriculture
 dig deeper into the isotopic constraints
FM Kai et al. Nature 476, 194-197 (2011) doi:10.1038/nature10259
31 Tg CH4 yr-1 decrease
(~6% total budget)
Variations in CH4 fluxes and the
impacts of source composition on
isotopic trends.
Conclusions from scenario analysis:
Assume all change due to FF,
IHD of 13C-CH4 widens, not
Consistent with obs
Agricultural source changes can
(or wetlands / better landfill
management)
 They posit wetland source hasn’t
changed in consistent way
FM Kai et al. Nature 476, 194-197 (2011) doi:10.1038/nature10259
Evidence for
intensification of rice
agriculture in Asia.
FM Kai et al. Nature 476, 194197 (2011)
doi:10.1038/nature10259
Increase in chemical fertilizer use
Increase in industrial water use;
New mid-season drainage of rice paddies
15.5 +/- 1.9 Tg CH4 yr-1 1984 to 2005
Follow-up (2012 Nature: Levin et al.)
Different isotope datasets
Do not support change in
IHD (so flat microbial source)
Response of Kai et al:
Need to bring together all
datasets; value of isotopic
measurements.
Hodson et al. GRL, 2011
Method: Use simple dynamic vegetation wetland model and compare with
ENSO index
Conclusions: Repeated El Nino events in 1980s and 1990s contributed to
reducing CH4 emissions and atmospheric abundance leveling off
E (x,t) = F(x) b Rh(x,t) S(x,t)
x= each 0.5° grid cell
t = monthly
E = wetland emission flux (Tg CH4 grid cell-1 month-1)
F=ecosystem dependent scaling factor
b = 0.03 mol CH4/mol C respired
Rh = heterotrophic respiration (mol C respired) from LPJ DGVM (T, CO2)
S = areal extent of wetland (satellite 1993-2000); fitted to runoff in LPJ
 Also account for differences in emitting capacity btw boreal + tropics (empirical)
Multivariate Enso index
“An index of six observed variables (such as pressure, air and sea-surface
temperatures, winds, cloudiness) over the tropical Pacific is used to monitor the
coupled ocean-atmosphere phenomenon known as the El Ni ño-Southern Oscillation
(ENSO). Areas with large positive values of the index (large red spikes) depict the "El
Niño" warm phase of the ENSO phenomenon. [From the NOAA Climate Diagnostics
Center”
http://www.research.noaa.gov/climate/observing1.html
Hodson et al., GRL, 2011: FIGURE 1
N. Temperate (27%)
and Tropics (44%)
Dominate variability
Tropics responds to
Variability in
inundated area;
Boreal to Rh (T)
R2 = 0.56
Hodson et al., GRL, 2011: TABLE 1
During events, wetland response > prior estimates for fires;
Possibility of offsetting influences during El Nino (+fires; -wetlands)
 Contributed to slow down (citing other work for anthropogenic sources)
Hodson et al., GRL, 2011: Table 2
Potential amplification if boreal wetland emissions increase in the future
Some overall discussion points
Why so many competing hypotheses?
How strong a constraint is there on the OH sink and trends therein?
Confidence in proxies we have for CH4 source attribution? How well
do we know isotopic source/sink signatures?
Representativeness of “reference” measurement stations
Large interannual “wiggles” in data: real? Artifacts of combining
measurements for different places / periods?
Connections of microbial emissions to other pollutants/GHGs
(acid deposition; N2O production)