GMDGeoscientific Model DevelopmentGMDGeosci. Model Dev.1991-9603Copernicus PublicationsGöttingen, Germany10.5194/gmd-11-4417-2018Advances in representing interactive methane in ModelE2-YIBs
(version 1.1)Advances in representing interactive methane in ModelE2-YIBsHarperKandice L.harper.kandice@gmail.comhttps://orcid.org/0000-0003-3752-7618ZhengYiqihttps://orcid.org/0000-0003-3445-5284UngerNadinen.unger@exeter.ac.ukhttps://orcid.org/0000-0001-7739-2290School of Forestry and Environmental Studies, Yale University, New
Haven, CT 06511, USADepartment of Geology and Geophysics, Yale University, New Haven, CT
06511, USACollege of Engineering, Mathematics and Physical Sciences, University
of Exeter, Exeter, EX4 4QJ, UKKandice L. Harper (harper.kandice@gmail.com) and Nadine Unger
(n.unger@exeter.ac.uk)2November201811114417443428March201818May201811September201827September2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://gmd.copernicus.org/articles/11/4417/2018/gmd-11-4417-2018.htmlThe full text article is available as a PDF file from https://gmd.copernicus.org/articles/11/4417/2018/gmd-11-4417-2018.pdf
Methane (CH4) is both a greenhouse gas and a
precursor of tropospheric ozone, making it an important focus of
chemistry–climate interactions. Methane has both anthropogenic and natural
emission sources, and reaction with the atmosphere's principal oxidizing
agent, the hydroxyl radical (OH), is the dominant tropospheric loss process
of methane. The tight coupling between methane and OH abundances drives
indirect linkages between methane and other short-lived air pollutants and
prompts the use of interactive methane chemistry in global
chemistry–climate modeling. In this study, an updated contemporary
inventory of natural methane emissions and the soil sink is developed using
an optimization procedure that applies published emissions data to the NASA
GISS ModelE2-Yale Interactive terrestrial Biosphere (ModelE2-YIBs) global
chemistry–climate model. Methane observations from the global surface
air-sampling network of the Earth System Research Laboratory (ESRL) of the
US National Oceanic and Atmospheric Administration (NOAA) are used to
guide refinement of the natural methane inventory. The wetland methane flux
is calculated as a best fit; thus, the accuracy of this derived flux assumes
accurate simulation of methane chemical loss in the atmosphere and accurate
prescription of the other methane fluxes (anthropogenic and natural). The
optimization process indicates global annual wetland methane emissions of
140 Tg CH4 yr-1. The updated inventory includes total global annual
methane emissions from natural sources of 181 Tg CH4 yr-1 and a
global annual methane soil sink of 60 Tg CH4 yr-1. An
interactive methane simulation is run using ModelE2-YIBs, applying dynamic
methane emissions and the updated natural methane emissions inventory that
results from the optimization process. The simulated methane chemical
lifetime of 10.4±0.1 years corresponds well to observed lifetimes.
The simulated year 2005 global-mean surface methane concentration is 1.1 %
higher than the observed value from the NOAA ESRL measurements.
Comparison of the simulated atmospheric methane distribution with the NOAA
ESRL surface observations at 50 measurement locations finds that the
simulated annual methane mixing ratio is within 1 % (i.e., +1 % to
-1 %) of the observed value at 76 % of locations. Considering the 50
stations, the mean relative difference between the simulated and observed
annual methane mixing ratio is a model overestimate of only 0.5 %.
Comparison of simulated annual column-averaged methane concentrations with
SCIAMACHY satellite retrievals provides an independent post-optimization
evaluation of modeled methane. The comparison finds a slight model
underestimate in 95 % of grid cells, suggesting that the applied methane
source in the model is slightly underestimated or the model's methane sink
strength is slightly too strong outside of the surface layer. Overall, the
strong agreement between simulated and observed methane lifetimes and
concentrations indicates that the ModelE2-YIBs chemistry–climate model is
able to capture the principal processes that control atmospheric methane.
Introduction
Atmospheric methane (CH4) is a greenhouse gas that warms the climate by
absorbing terrestrial radiation. The industrial-era increase in the methane
concentration (+150 %) has induced a global-mean radiative forcing
(+0.48±0.05 W m-2) that is the second largest in magnitude
among all well-mixed greenhouse gases, smaller only than that induced by the
increase in atmospheric carbon dioxide (CO2, +1.82±0.019 W m-2) (Myhre et al., 2013).
On a 20-year timescale, the global warming
potential of methane is a factor of 84 larger than that for CO2 (Myhre
et al., 2013). In addition to its role as a climate forcer, methane affects
air quality through its role as a precursor of the harmful air pollutant
tropospheric ozone (West and Fiore, 2005).
Methane is emitted to the atmosphere by both anthropogenic and natural
sources (Ciais et al., 2013; EPA, 2010; Kirschke et al., 2013), including
incomplete combustion of fossil fuels, biofuels, and plant biomass; seepage
from terrestrial and marine reservoirs; and through the action of
methanogenic bacteria, which produce methane through anaerobic breakdown of
organic matter. Methane generation through bacterial decomposition of
organic matter occurs in wetland soils; waterlogged agricultural soils,
such as rice paddies; landfills; and in the digestive systems of ruminant
animals and termites (Cicerone and Oremland, 1988). Removal of atmospheric
methane occurs primarily through oxidation by the hydroxyl radical (OH), the
atmosphere's principal oxidizing agent (Logan et al., 1981). Additional
chemical loss occurs in the stratosphere via reaction with chlorine radicals
and excited-state oxygen radicals (O1D; Kirschke et al., 2013;
Portmann et al., 2012). Uptake and oxidation of methane by methanotrophic
bacteria in dry, aerated soils serves as an additional small sink (Kirschke
et al., 2013).
The contemporary methane abundance and growth rate are well known owing to
high-precision surface observations made by global monitoring networks, such
as that coordinated by the Earth System Research Laboratory/Global
Monitoring Division (ESRL/GMD) of the National Oceanic and Atmospheric
Administration (Dlugokencky et al., 2015). Methane chemical lifetime is not
directly measured in the atmosphere, but has been derived from knowledge of
the synthetic compound methyl chloroform (CH3CCl3; Prather et al.,
2012; Prinn et al., 2005; Rigby et al., 2013). Methyl chloroform has
well-known anthropogenic emissions and no natural emission source. Similar
to methane, the principal sink of atmospheric methyl chloroform is oxidation
by OH. Observations of methyl chloroform abundance, in conjunction with
estimates of methyl chloroform emissions, provide a means to estimate global
OH abundance, methyl chloroform lifetime, and, subsequently, methane
lifetime (Prinn et al., 1995). Together, these estimates provide a
constraint on the total methane flux into the atmosphere; however,
apportionment of this total into contributions from the individual source
sectors is highly uncertain (Kirschke et al., 2013; Saunois et al., 2016).
Because reaction with OH is the primary sink of methane, a change in the
abundance of OH can alter methane's atmospheric burden and lifetime and,
consequently, its capacities to both influence climate and generate ozone
(Fry et al., 2012; Fuglestvedt et al., 1996). Emissions of nitrogen oxides
(NOx) decrease methane by increasing the oxidation capacity of the
atmosphere, while emissions of non-methane volatile organic compounds
(NMVOCs) and carbon monoxide (CO) increase methane by consuming atmospheric
OH (Fry et al., 2012; Naik et al., 2005). Increased emissions of methane can
prolong methane's own atmospheric lifetime (Fuglestvedt et al., 1996).
Methane emissions can likewise influence the concentrations of other climate
forcing pollutants; for example, the atmospheric burden of sulfate aerosols
is influenced not only by emissions of the precursor gas sulfur dioxide
(SO2) but also by emissions of CO, CH4, NMVOCs, and NOx,
which influence the conversion of SO2 to sulfate aerosols by affecting
the burdens of a variety of tropospheric oxidants (Shindell et al., 2009;
Unger et al., 2006).
The strong oxidant-driven linkages among the short-lived air pollutants
demonstrate the need to use global modeling to study chemistry–climate
interactions, including those involving methane. In chemistry–climate model
simulations, atmospheric methane is commonly represented through
prescription of its surface concentration (Naik et al., 2013). Simulations
using interactive methane (Shindell et al., 2013), in which the online
methane concentration is dynamically tied to oxidant availability, can
provide an improved understanding of chemistry–climate interactions. A
spatially explicit methane emissions inventory is necessary for running
interactive climate simulations that apply dynamic methane emissions. In
this study, published sector-specific data on natural methane fluxes (Ciais
et al., 2013; Dutaur and Verchot, 2007; EPA, 2010; Etiope et al., 2008; Fung
et al., 1991; Kirschke et al., 2013; Melton et al., 2013; Saunois et al.,
2016; Schwietzke et al., 2016) are used in conjunction with atmospheric
modeling and atmospheric methane observations (Dlugokencky et al., 2015) to
guide development of a spatially explicit contemporary budget of natural
methane emissions and the methane soil sink. The NASA ModelE2-Yale
Interactive terrestrial Biosphere (ModelE2-YIBs) global chemistry–climate
model (Schmidt et al., 2014; Shindell et al., 2013; Yue and Unger, 2015) is
subsequently used to run an interactive methane simulation representative of
year 2005 that applies the refined natural methane flux inventory. The
simulated atmospheric methane distribution is evaluated against multiple
observational datasets. Because methane is an ozone precursor, a comparison
of simulated ozone mixing ratios with a contemporary ozone climatology is
also presented.
Interactive methane in ModelE2-YIBs
Atmospheric modeling, using ModelE2-YIBs, was used to develop an updated
natural methane emissions inventory. The updated inventory is required for
global chemistry–climate simulations that employ interactive methane
emissions. A three-step methodology was applied. First, gridded input files
of the natural methane emission sources and soil sink were built using
published inventories and flux information (Ciais et al., 2013; Dutaur and
Verchot, 2007; EPA, 2010; Etiope et al., 2008; Fung et al., 1991; Kirschke
et al., 2013; Melton et al., 2013; Saunois et al., 2016; Schwietzke et al.,
2016). Secondly, ModelE2-YIBs simulations were performed; the simulations
applied the natural methane emissions inventory and year 2005 emissions for
all other emission sources of short-lived air pollutants. ModelE2-YIBs is
described in Sect. 2.1, and the interactive methane simulation configuration
and forcing datasets are described in Sect. 2.2. Thirdly, the modeled
atmospheric methane distribution resulting from the second step was compared
to methane surface observations at 50 globally distributed locations. The
NOAA ESRL methane measurements (Dlugokencky et al., 2015) are described in
Sect. 4. The model–measurement comparison was used to refine the spatial
and temporal distribution of methane emissions from wetlands. The second and
third steps were repeated, applying the newly optimized wetland emissions to
ModelE2-YIBs, until strong model–measurement agreement was achieved. The
resulting natural methane emissions inventory is described in Sect. 3, along
with additional details about the optimization process for the wetland
methane source. Evaluation of the simulated methane distribution based on
the final inventory is presented in Sect. 4. Comparison of the modeled
methane distribution with column-averaged methane concentrations derived
from SCIAMACHY satellite retrievals (Schneising et al., 2009) serves as an
independent validation of the simulated methane distribution.
Using ModelE2, Shindell et al. (2013) previously used a similar procedure of
modifying the wetland methane source to achieve a modeled methane
concentration that is in line with present-day observations, noting that the
accuracy of the magnitude of the wetland flux that is derived in this way
depends on whether the other prescribed fluxes have been accurately
assigned. That is, the applied methodology calculates the wetland methane
emission magnitude as a best fit under the assumption that the other methane
fluxes and simulated atmospheric chemical loss are accurately represented in
the global model. Relative to the Shindell et al. (2013) study, this study
updates the natural non-wetland methane fluxes, applies a different
anthropogenic emissions inventory, includes a new land surface model with
interactive computation of isoprene and monoterpene emissions (Unger et al.,
2013; Yue and Unger, 2015), and applies observed ocean boundary conditions.
This methodology permits harmonization of the modeled methane mole fractions
with contemporary observations, but can potentially misattribute the methane
fluxes among the various source categories. Planned chemistry–climate
simulations that will make use of the natural methane inventory developed
here are specifically designed to investigate perturbations in anthropogenic
methane emissions (i.e., the natural methane fluxes will be held constant
using the magnitudes and distributions determined here). Any inaccuracies in
assignment of the methane fluxes among the natural source sectors are
relatively unimportant for the purposes of such studies.
The model input files prescribing the natural non-wetland methane sources
have been developed based on the best available information (Sect. 3). For
estimates of the global annual wetland methane flux, a recent model
intercomparison reported variation of ±40 % around the
multi-model mean for seven models that were driven with the same climate
conditions and atmospheric CO2 concentrations (Melton et al., 2013). It
is because of the large uncertainty in the contemporary magnitude of the
wetland methane flux (Kirschke et al., 2013; Melton et al., 2013) that the
emissions from this sector are optimized using atmospheric modeling.
Model description
The ModelE2-YIBs global chemistry–climate model is the result of the
two-way coupling of the YIBs land surface model (Yue and Unger, 2015) with
the NASA Goddard Institute for Space Studies (GISS) ModelE2 general circulation model (Schmidt et al., 2014).
ModelE2-YIBs has a horizontal resolution of 2∘ latitude × 2.5∘ longitude with 40 vertical layers covering the global
atmosphere from the surface to the 0.1 hPa model top. Physical and chemical
processes are computed at a 30 min time step.
The atmospheric chemical mechanism features 51 chemical species
participating in 156 chemical reactions (Schmidt et al., 2014; Shindell et
al., 2006). Twenty seven chemical tracers are advected according to the
model dynamics (Shindell et al., 2006). The troposphere and stratosphere are
coupled in terms of both dynamics and chemistry (Shindell et al., 2006).
Stratospheric chemistry includes nitrous oxide (N2O) and halogen
chemistry (Shindell et al., 2006). The troposphere includes standard
NOx-Ox-HOx-CO-CH4 chemistry; methane, isoprene,
monoterpenes (as α-pinene), and formaldehyde are explicitly
represented in the model, while other hydrocarbons are represented using a
lumped scheme (Houweling et al., 1998) that is based on the Carbon Bond
Mechanism IV
(Gery et al., 1989) and the Regional Atmospheric Chemistry Mechanism
(Stockwell et al., 1997). More recent updates to the chemical mechanism are
described by Shindell et al. (2006, 2013). The alkane and alkene lumped
hydrocarbon classes used in the ModelE2-YIBs chemical mechanism are
calculated from the total NMVOC emissions from the prescribed emissions
scenario (described in Sect. 2.2) by applying spatially explicit
alkane-to-total-NMVOC and alkene-to-total-NMVOC ratios from the RCP8.5
inventory (Riahi et al., 2011) for year 2005.
In this study, methane is calculated as an interactive tracer that is driven
by methane surface fluxes, is influenced by oxidant chemistry, and, in turn,
affects online oxidant availability (Shindell et al., 2013). This paper
describes the new version 1.1 of ModelE2-YIBs. ModelE2-YIBs version 1.1
refers to the use of interactive methane chemistry and dynamic methane
emissions (including application of the final contemporary natural methane
flux inventory described in Sect. 3) within the framework of ModelE2-YIBs
version 1.0. ModelE2-YIBs version 1.0 refers to YIBs version 1.0 (Yue and
Unger, 2015) coupled to the version of ModelE2 described by Schmidt et al. (2014). For anthropogenic and biomass burning sectors, emissions are
prescribed for reactive gas and primary aerosol species. Biomass burning
emissions are mixed into the atmospheric boundary layer. Vertically resolved
NOx aviation emissions are injected at 25 levels that extend to an
altitude of ∼15 km. Prescribed emissions from all sectors
other than biomass burning and aviation are treated as surface fluxes. Daily
surface fluxes are interactively interpolated from the relevant monthly or
annual prescribed fluxes.
Climate-sensitive interactive emissions include isoprene (Arneth et al.,
2007; Unger et al., 2013), monoterpenes (Lathière et al., 2006), mineral
dust (Miller et al., 2006), oceanic dimethyl sulfide (Koch et al., 2006),
sea salt particles (Koch et al., 2006), and lightning NOx (Price et
al., 1997). Interactive radiatively active secondary inorganic aerosols
include nitrate (Bauer et al., 2007) and sulfate (Koch et al., 2006).
Secondary organic aerosols are formed from the interactive emissions of
isoprene, monoterpenes, and other reactive VOCs
(Tsigaridis and Kanakidou, 2007). Gas-phase aerosol precursors and oxidants
affect the production and processing of aerosols (Bell et al., 2005), and
aerosol-induced perturbations to the radiation budget impact photolysis
rates (Bian et al., 2003). The online climate state provides the
meteorological parameters that affect atmospheric chemistry, such as
humidity, temperature, and sunlight. ModelE2 has previously undergone
rigorous validation of simulated present-day tropospheric and stratospheric
chemical composition and circulation (Shindell et al., 2006, 2013).
Extensive evaluation of the atmospheric methane distribution that is
simulated using the updated inventory of contemporary natural methane fluxes
is presented in Sect. 4.
Simulation configuration
The atmosphere-only, time-slice simulation E2005 is representative of year
2005 and is run using interactive methane chemistry, including the use of
dynamic methane emissions. The simulations were performed on the Omega
cluster at the Yale Center for Research Computing. Omega is a
704-node 5632-core cluster based on the Intel Nehalem nodes and equipped
with 36 GB of RAM per node, a QDR Infiniband interconnect, and a high-speed
Lustre DDN file system for parallel I/O. When the cluster was operating at
peak performance, NASA ModelE2-YIBs had a runtime of 8–10 model days per
hour using 88 processors.
Two datasets are used to define global anthropogenic and biomass burning
emissions of the short-lived air pollutants for 2005: (1) a scenario derived
from the Greenhouse gas–Air pollution Interactions and Synergies (GAINS)
integrated assessment model (Amann et al., 2011; http://gains.iiasa.ac.at, last access: 25 October 2017)
and (2) the RCP8.5 emissions scenario (Riahi et al., 2011). GAINS emission
scenarios are composed of three basic elements (Amann et al., 2011): (1) activity
pathways that describe the temporal evolution of polluting
activities, (2) region-specific emission factors for all emitted pollutants
from all polluting activities, and (3) control strategies that define the
degree of penetration of available pollution control technologies over time.
The GAINS-derived global scenario for the short-lived air pollutants was
created by combining existing scenario elements from the GAINS database: the
activity pathway for the agriculture sector is based on estimates by the
Food and Agriculture Organization (Alexandratos and Bruinsma, 2012) and
those for the industrial process, mobile transport, and VOC-specific sectors
are based on projections from the International Energy Agency (IEA, 2011);
the energy sector activity pathway includes regional-level data from China
(Zhao et al., 2013); and the pollution control strategy makes use of
extensive updates for methane emission sources (Höglund-Isaksson, 2012).
The GAINS air pollution emissions scenario defines emissions from the
anthropogenic sectors: agriculture, agricultural waste burning, domestic,
energy, industrial, solvents, transportation, and waste. As the GAINS
integrated assessment model does not project emissions from aviation,
international shipping, or biomass burning (savanna, grassland, and
forest fires) sectors, the E2005 simulation assigns the RCP8.5 emissions of
short-lived climate pollutants and their precursors for these sectors (Riahi
et al., 2011). Information from the GAINS model was used to develop the
trajectory of future air pollution emissions in the RCP8.5 scenario (Riahi
et al., 2011). Prescribed global annual-mean surface-level mixing ratios of
the non-methane well-mixed greenhouse gases are likewise from the RCP8.5
scenario (Meinshausen et al., 2011; Riahi et al., 2007): 379.3 ppmv
CO2, 319.4 ppbv N2O, and 793 pptv chlorofluorocarbons (CFCs =
CFC-11 + CFC-12).
Prescribed monthly-varying sea ice concentrations and sea surface
temperatures are derived from the global observation-based Hadley Centre Sea
Ice and Sea Surface Temperature dataset (Rayner et al., 2003), using
averages over the years 2003–2007. The simulated concentrations of ozone,
methane, and aerosols are allowed to affect the model radiation and,
therefore, meteorology and dynamics. In other words, these simulations allow
rapid adjustments to the climate system (Myhre et al., 2013), and such
climate perturbations can, in turn, affect the simulated atmospheric
composition.
For simulations using the interactive methane scheme in ModelE2, the
atmospheric methane distribution at initialization is defined through
application of a vertical gradient, derived from HALOE observations (e.g.,
Russell III et al., 1993), to prescribed hemispheric-mean surface methane
concentrations (Dlugokencky et al., 2015). The E2005 simulation applies the
final contemporary natural methane flux inventory described in Sect. 3 that
was developed using the optimization process. For most sectors,
anthropogenic and natural methane emissions are prescribed in the climate
model using global, gridded input files; lake, oceanic, and terrestrial
geological methane emissions are internally calculated by the model through
prescription of emission factors in the model source code. Using an
interactive methane configuration with dynamic methane emissions, the
simulated atmospheric methane mixing ratio is temporally and spatially
variable.
The E2005 simulation was run until atmospheric methane reached steady state,
such that the global chemical sink came into balance with the net global
source (prescribed sources minus prescribed soil sink), resulting in a
relatively stable atmospheric methane abundance. Steady-state conditions
were diagnosed using the global annual-mean atmospheric burden of methane.
The final 10 years of the 45-year simulation are used for analysis.
Year-to-year variation in the methane burden for the final 10 model years is
<3.2 Tg CH4. Year-to-year variation in the global-average
surface methane concentration is <1.3 ppbv. The year of interest
for this study, 2005, fell within a roughly 8-year period that witnessed a
largely stable global-mean concentration of methane in Earth's atmosphere
(Dlugokencky et al., 2009). The observed stability in the concentration of
methane does not necessarily indicate temporally invariant global sources
and sinks over this era (Rigby et al., 2017; Turner et al., 2017). For
example, a recent analysis by Turner et al. (2017) suggests that
simultaneous counterbalancing changes in methane emissions and loss to OH
may be responsible for the observed stability in the methane concentration
in the early 2000s. Therefore, the methane budget derived in this study by
assuming steady-state conditions represents just one plausible solution that
can lead to a stable atmospheric methane concentration. This assumption is
convenient in global chemistry–climate modeling where the simulated climate
state does not correspond to an exact meteorological year. The derived
solution is constrained by both the prescribed methane fluxes and other
forcing data that can affect atmospheric methane, such as emissions of
other short-lived compounds; the prescribed ocean conditions, which
influence the physical climate state; and the concentrations of the
non-methane long-lived greenhouse gases, which influence the radiation
budget. The non-wetland natural methane fluxes that are prescribed are based
on published estimates (Sect. 3) and are representative of the 2000s
contemporary era but are not necessarily specific to year 2005. Likewise,
the prescribed sea ice distribution and sea surface temperatures are
observation-based 5-year means centered on year 2005. The derived methane
budget, therefore, represents a 2000s climatology and is approximately, but
not precisely, representative of year 2005 conditions.
The global annual emission magnitudes of the non-methane short-lived air
pollutants for E2005 are summarized in Table 1; the methane budget is
discussed in Sect. 3. The global annual-mean surface air temperature for
E2005 is 14.6±0.03∘C (average ±1 standard
deviation, calculated over 10 model years).
Global annual emissions of reactive non-methane gases and
aerosols.
* During a simulation, the emission magnitudes of the interactive sectors
exhibit interannual variability. The value listed for the interactive
emissions is the average calculated over 10 model years. The standard
deviation over 10 model years is 0.08 TgN yr-1 for lightning NOx;
0.56 Tg yr-1 for DMS; 4.9 TgC yr-1 for isoprene; and 1.8 TgC yr-1 for monoterpenes.
Contemporary natural methane emissions and soil sink
The contemporary natural methane budget used in this study is shown in Table 2.
The non-wetland natural methane fluxes are derived from published
estimates. The wetland methane emissions shown in Table 2 are the final
result of the iterative optimization process introduced in Sect. 2 and
described in more detail below.
Many of the natural methane emission input files used here were created by
updating gridded emission files from a dataset produced by Fung et al. (1991). To construct best estimates of the spatial and temporal distribution
of methane fluxes for the 1980s, Fung et al. (1991) first combined flux
measurements, isotopic profiles, and land surface data to generate plausible
flux scenarios and then refined the resultant scenarios using tracer
transport modeling in conjunction with observations of the atmospheric
methane concentration. For the natural methane budget in this project, the
spatial distribution of the fluxes prescribed by Fung et al. (1991) was
largely retained for most sources and for the soil sink, while the regional
or global flux totals were scaled to match more recent estimates.
Global anthropogenic methane emissions for 2005 from the GAINS scenario are
325.1 Tg yr-1. This total excludes emissions from international
shipping, which are not quantified in the GAINS model, and are instead
prescribed following the RCP8.5 trajectory (Riahi et al., 2011). RCP8.5
methane emissions from international shipping for 2005 are 0.5 Tg yr-1,
accounting for a negligible fraction of total anthropogenic methane
emissions. GAINS-derived anthropogenic methane emissions differ from those
in the RCP8.5 inventory (Riahi et al., 2011) by
∼1 %, indicating good agreement in global magnitude.
Fung et al. (1991) geographically distributed annual methane emissions from
termites based on habitat distribution information. Here, the Fung et al. (1991) spatial distribution of the methane emissions from termites is
retained, and the global annual flux is scaled to 6 Tg yr-1, which is
the first quartile of the range of published estimates reported both by a
recent review (Kirschke et al., 2013) and by the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change (Ciais et al., 2013). The assigned value is close in
magnitude to that suggested by a recent estimate (9 Tg yr-1, range:
3–15 Tg yr-1) that was determined by upscaling ecosystem-specific
emission factors (Saunois et al., 2016).
An assessment of the methane budget by the US Environmental Protection
Agency (EPA) notes that various inventories might differentially apportion
emissions to related source categories, such as for wetland and lake sources
or for the various terrestrial and oceanic sources (e.g., gas hydrate, in
situ ocean, estuarine, and geological sources; EPA, 2010). Conservative
estimates of the ocean, freshwater, and geological sources are applied to
the inventory created here to avoid overcounting methane emissions from
these categories since different literature references were used to assign
the fluxes for these sources. For example, the lake source in this inventory
is assigned as 10 Tg yr-1, evenly distributed over global lake area,
which is the lower end of the range (10–50 Tg yr-1) of published
estimates that have been collated by the EPA assessment (EPA, 2010).
Based on published estimates, the EPA assessment reports an ocean methane
source in the range of 2.3–15.6 Tg yr-1, but notes that some of this
methane source is likely geological or hydrates (EPA, 2010). The combined
ocean plus estuarine source in this inventory is 5 Tg yr-1,
corresponding roughly to the first quartile of the suggested range. The
marine methane flux is evenly divided over the global ocean.
A conservative terrestrial geological source of 20 Tg yr-1 is assigned.
Owing to the very large uncertainty in spatial and temporal placement of the
fluxes (Etiope et al., 2008), the terrestrial geological component is evenly
divided over the Earth's land surface in this inventory. Recent isotopic
analyses suggest that the total geological source assigned here might be
underestimated (Schwietzke et al., 2016). The total fossil fraction of
methane emissions in the inventory developed here is ∼31 %, including industrial fossil fuel use, terrestrial geological, and
oceanic sources. Based on their reported sector-mean emissions, the total
fossil fraction for the period 2003–2013 from the recent Schwietzke et al. (2016)
analysis is calculated as ∼33 %. Their inventory
represents an increase in fossil-based methane emissions relative to
previous budgets (Schwietzke et al., 2016). While the fossil fraction for
the inventory built here largely matches that of the Schwietzke et al. (2016) analysis, the total magnitude of fossil-based emissions are higher in
the Schwietzke et al. (2016) inventory, including geological emissions that
are a factor of 2 stronger than those assigned here. While the gross
magnitude of methane emissions is well constrained, substantial
uncertainties remain regarding the partitioning of methane emissions among
source categories (Rigby et al., 2017; Turner et al., 2017). The
interpretation of isotope composition measurements is currently ambiguous
and complex (Turner et al., 2017). Prather and Holmes (2017) have recently
suggested new approaches to extract more useful information from existing
observations by exploiting spatial patterns.
Some small, uncertain source sectors were not included in the methane budget
used in this project. For example, annual methane emissions from permafrost
are estimated to be 1 Tg yr-1 or less (EPA, 2010; Kirschke et al.,
2013), but these estimates are likely upper bounds as they do not account
for oxidation of the methane as it travels through the overlying soil to
reach the atmosphere (EPA, 2010). No separate permafrost source is included
in this inventory.
Using the natural methane flux estimates described here in conjunction with
anthropogenic and biomass burning emissions of the short-lived air
pollutants from the GAINS and RCP8.5 scenarios, the optimization process
employing ModelE2-YIBs finds that the present-day methane source from
wetlands is 140 Tg yr-1 when a soil sink of 60 Tg yr-1 is applied.
In the Wetland and Wetland CH4 Inter-comparison of Models Project
(WETCHIMP) assessment, seven models reported interactive global methane
emissions from wetlands (Melton et al., 2013). The multi-model mean ±1 standard deviation is 190±39 Tg yr-1 for the WETCHIMP study,
with individual models reporting values of 141–264 Tg yr-1 (Melton et
al., 2013). Thus, the wetland methane emission magnitude used in
ModelE2-YIBs is 26 % lower than the WETCHIMP multi-model mean, but almost
identically corresponds to the results from one of the individual models,
indicating that the prescribed emission magnitude for this highly uncertain
sector is reasonable.
The iterative refinement process used to optimize the wetland methane flux
was largely a trial-and-error based methodology that made use of
literature-derived estimates and surface observations. The wetland methane
flux is calculated as a best fit following prescription of the other fluxes.
The baseline wetland methane emissions applied to the optimization process
are the methane emissions from bogs and swamps from Fung et al. (1991); the
magnitude, spatial distribution, and temporal distribution of these
emissions were subsequently modified to varying degrees during the
optimization process. At each step of the process, the annual cycle of
modeled surface-level methane concentration was compared to observations
from the NOAA ESRL measurement network at 50 globally distributed sites
(Dlugokencky et al., 2015). The aim of the optimization process was to
minimize the absolute value of the normalized mean bias (NMB) at the largest
number of sites. Considering the full set of 50 sites, the final optimized
scenario results in NMBs ranging from -1.3 % (model underestimate) to
+3.0 % (model overestimate), with a median of +0.4 %. At three-quarters
of sites, the NMB is between -1 % and +1 %. An evaluation
of the simulated atmospheric methane distribution associated with the final
optimized emissions inventory, including a comparison to SCIAMACHY methane
columns (Schneising et al., 2009), is provided in Sect. 4. Modification of
the temporal distribution of wetland methane emissions was guided by both
the annual cycles of surface methane concentrations at the 50 NOAA ESRL
measurement sites (Dlugokencky et al., 2015) and the annual cycle of wetland
methane emissions simulated by the models participating in the WETCHIMP
analysis (Melton et al., 2013).
The best fit of modeled atmospheric methane relative to the NOAA ESRL
surface methane observations corresponds to the following modification of
the baseline wetland methane emissions dataset. First, the baseline wetland
methane emissions (extratropical bogs and tropical swamps) from Fung et al. (1991) were scaled to achieve an extratropical emissions fraction of 30 %
and a prescribed global emission magnitude of about 130 Tg CH4 yr-1. A single scaling factor was applied in each grid cell in each
month to the emissions from bogs; likewise, a separate single scaling factor
was applied in each grid cell in each month to the emissions from swamps.
For the WETCHIMP study, the mean extratropical emissions fraction among all
participating models is about 30 % (Melton et al., 2013). Secondly, an
additional 10 Tg CH4 yr-1 was added to the wetland methane
emissions: (1) 2 Tg CH4 yr-1 was added to 20–40∘ N
over the months March through September, (2) 2 Tg CH4 yr-1 was added to 0–20∘ N over the
months May through October, and (3) 6 Tg CH4 yr-1 was added to
20∘ S–0∘ over all months. Finally, the seasonal cycle
of the wetland methane emission hotspots in Finland and Russia
(50–70∘ N) were adjusted: 0.5 Tg month-1
decrease for each of June, July, and August; 0.65 Tg month-1 increase
in both September and October; and 0.2 Tg month-1 increase in November.
The methane soil sink in the ModelE2-YIBs inventory corresponds to the top
end of the range suggested by the review of Dutaur and Verchot (2007) but is
higher than the magnitude reported in recent reviews (e.g., top-down range:
26–42 Tg yr-1, bottom-up range: 9–47 Tg yr-1; Kirschke et al.,
2013). The wetland methane emissions are derived as a best fit given the
other prescribed emissions, the methane soil sink, and the simulated
chemical sink. Applying a weaker soil sink would have resulted in a lower
magnitude for the derived wetland methane emissions; applying a stronger
soil sink would have resulted in a higher magnitude for the derived wetland
methane emissions. The simulated total atmospheric lifetime of methane and
the simulated methane mixing ratio in ModelE2-YIBs are well aligned with
observation-based estimates (Sect. 4), suggesting that the overall rate of
removal of methane is well represented in the model.
Monthly wetland methane emissions (Tg CH4 month-1) for several latitudinal bands for the optimized inventory.
The annual cycle of wetland methane emissions is plotted in Fig. 1. Monthly
emissions are shown for the same latitudinal zones that are plotted in
Melton et al. (2013) for six models participating in the WETCHIMP analysis
(their Fig. 6, corresponding to the mean annual cycle for years 1993–2004).
Global monthly methane emissions from wetlands range from 7.4 to 18.2 Tg month-1 (Fig. 1). Monthly emissions show little variability from
November to April (range: 7.4–9.5 Tg month-1), followed by increasing
emissions starting in May (12.9 Tg month-1). Peak monthly emissions
occur in July (18.2 Tg month-1). The six WETCHIMP models simulate peak
emissions, variously occurring between June and August, of slightly higher
magnitude (approximate range for the six models: 20–35 Tg month-1;
Melton et al., 2013). The annual cycle of emissions for the 40–90∘ N latitudinal band is similar in shape to that for global
emissions, with peak monthly emissions likewise occurring in July (9.1 Tg month-1; Fig. 1). Monthly emissions for the 20–40∘ N
band show little variation throughout the year and are of
low magnitude (range: 0.5–0.9 Tg month-1; Fig. 1), while the WETCHIMP
models generally exhibit a small peak on the order of 5 Tg month-1 in
this band in the Northern Hemisphere summer (Melton et al., 2013). The
0–20∘ N band shows increasing monthly emissions
between February and August, followed by declining monthly emissions (Fig. 1).
The 20∘ S–0∘ band shows the largely opposite cycle,
with minimum monthly emissions occurring in August (1.4 Tg month-1).
Monthly emissions from the tropics, considering 30∘ S–30∘ N, are largely consistent throughout the year, ranging from
6.0–8.0 Tg month-1.
The zonal distribution of annual wetland methane emissions is shown in Fig. 2,
with emissions aggregated over 2∘-latitude bands. Peak annual
emissions occur near the Equator, similar to the WETCHIMP multi-model mean
(Melton et al., 2013, their Fig. 5, although shown in 3∘-latitude
bands). In the Southern Hemisphere, the optimized wetland methane inventory
exhibits smaller secondary peaks near 15 and 30∘ S.
The WETCHIMP multi-model mean likewise exhibits regional peaks in these
locations, but the magnitude of the peak at 30∘ S relative to the
peak at the Equator is stronger in the optimized inventory than in the
WETCHIMP analysis. Like the WETCHIMP multi-model mean, the optimized wetland
emissions inventory shows a wide secondary peak centered around
55∘ N. The secondary peak at 10∘ N is also seen in the
WETCHIMP multi-model mean; in the optimized inventory, this peak exhibits a
stronger magnitude relative to the main peak at the Equator than occurs in
the WETCHIMP analysis. The spatial distributions of the monthly wetland
methane emissions are shown in Fig. S1 in the Supplement, and the gridded optimized monthly
wetland methane emissions data are provided in the Supplement.
Annual zonally summed wetland methane emissions (Tg CH4 2∘-latitude band-1 yr-1) for the optimized
inventory.
Regional annual methane emissions from non-oceanic sources
(Tg yr-1). Regional definitions follow Saunois et al. (2016).
RegionAnnual methane emissions(Tg yr-1)Temperate South America23.0Tropical South America70.4Central North America12.1Contiguous USA37.0Boreal North America17.7Southern Africa37.8Northern Africa38.4Europe30.6Russia60.7Central Eurasia and Japan57.2China50.5India26.3Southeast Asia47.4Oceania17.1
Total annual methane emissions from all non-oceanic sources are shown in
Table 3 for 14 regions. Regional definitions follow Saunois et al. (2016).
In their Table 4, Saunois et al. (2016) provide estimates of annual methane
emissions (means for 2000–2009) for the same 14 regions, including both
best estimates and ranges resulting from a set of inversions. The regional
methane emissions from the optimized inventory fall within the suggested
range of Saunois et al. (2016) for nine regions: temperate South America,
tropical South America, central North America, boreal North America,
southern Africa, northern Africa, Europe, China, and Oceania. For two other
regions (contiguous USA and India), the emissions fall within 1–2 Tg yr-1 of the suggested range. Emissions in Southeast Asia from the
optimized inventory are slightly lower than the range of 54–84 Tg yr-1
suggested by Saunois et al. (2016). The optimized inventory exhibits
emissions that are higher than the suggested ranges of Saunois et al. (2016)
for two regions: (1) Russia (suggested range: 32–44 Tg yr-1) and (2) central
Eurasia and Japan (suggested range: 38–51 Tg yr-1). For both
regions, the strong emissions in the inventory applied here are associated
with strong energy sector emissions and, in the case of Russia, strong
wetland emissions. Comparison of simulated column-averaged methane
concentrations with those from SCIAMACHY (Sect. 4.2) shows model
underestimates on the order of 2 % in these regions, which is typical of
model underestimates in other regions. The global distributions of annual
methane emissions by source category are shown in Fig. S2. The total
emission magnitude of methane for 2005 in the ModelE2-YIBs inventory is 532 Tg yr-1 (Table 2), which corresponds well to the top-down estimate
(548 Tg yr-1, range: 526–569 Tg yr-1) reported by the Kirschke et al. (2013) review and is only slightly outside of the range from the top-down
estimate (552 Tg yr-1, range: 535–566 Tg yr-1) reported by the more
recent Saunois et al. (2016) review.
Simulated methane in ModelE2-YIBs
The annual-mean mixing ratio of surface-level methane for E2005 is plotted
in Fig. 3. The global map indicates strong spatial heterogeneity, with local
surface concentrations ranging from 1664 to 2198 ppbv. Source regions with
strong methane emissions are readily apparent, such as parts of Russia,
South America, and central Africa (large wetland sources) and the Middle
East and China (large anthropogenic sources, including agricultural sources
in the case of China). The model output indicates a large interhemispheric
difference in surface-level methane concentrations, driven by comparatively
strong emissions in the Northern Hemisphere (NH) relative to the Southern
Hemisphere (SH).
Based on application of the year 2005 emission inventory to ModelE2-YIBs,
the simulated hemispheric-mean surface methane mixing ratios are 1746 ppbv
for the SH and 1841 ppbv for the NH. The simulated global-mean surface
methane concentration of 1793 ppbv is only 1.1 % higher than the observed
value for 2005 derived from the NOAA ESRL global air-sampling network
(Dlugokencky et al., 2015). The small model overestimate is only slightly
higher in the methane-emissions-rich NH (+1.3 %) than in the
comparatively methane-emissions-poor SH (+0.9 %). Both the model and
the NOAA ESRL measurements indicate an interhemispheric ratio (NH : SH) of
1.05. This comparison indicates that the broad pattern of surface methane
concentration simulated by the model is realistic.
A spatially explicit validation of the simulated atmospheric methane
distribution is achieved through comparison of the E2005 output with (1) NOAA ESRL surface measurements from 50 globally distributed stations
(Dlugokencky et al., 2015), described in Sect. 4.1, and (2) methane columns
derived from the SCIAMACHY instrument aboard the ENVISAT satellite
(Schneising et al., 2009), described in Sect. 4.2.
Comparison with surface measurements
The model–measurement comparison making use of the NOAA ESRL surface
measurements (Dlugokencky et al., 2015) is performed for each measurement
station that has at least one data point available per calendar month for
the period 2001–2005. The locations of the 50 measurement stations that
fulfill this criterion are identified on the map in Fig. S3. These 50
measurement stations collectively span latitudes extending from
90∘ S to 82.5∘ N. Roughly three-quarters of
the measurement stations are located in the NH. There is a
dearth of land-based measurement sites located in South America, Africa, and
Australia. For each measurement site, the analysis uses all monthly
observations available for the period 2001–2005 along with the E2005 output
for the overlapping model grid cell.
Simulated annual-mean surface methane mixing ratio (ppbv)
for year 2005.
A latitudinal gradient in the annual-mean surface methane mixing ratio is
evident in both the observations and model results (Fig. 4). The relative
difference between model and observation ranges from a model underestimate
of 1.3 % in Moody, Texas, (31.3∘ N, 97.3∘ W) to a model overestimate of 3.0 % on the Tae-ahn Peninsula
(36.7∘ N, 126.1∘ E). The simulated methane
concentration is within 1 % (i.e., -1 % to +1 %) of the measured
value at 76 % of locations. Only three sites exhibit an overestimate
>2 %. Considering all 50 sites, the average relative
difference between model and observations is a model overestimate of 0.5 % (median =0.4 %), indicating that the model skillfully simulates
annual-mean surface methane mixing ratios.
Figure 5 shows the annual cycles for the 50 measurement stations. The
individual panels also report the NMB (%)
calculated using monthly means for each measurement location;
mathematically, the NMB based on monthly means is equal to the relative
difference (%) in annual means. At most measurement sites, the simulated
annual cycle of surface methane largely mimics the observed cycle. In the
SH middle to high latitudes, the model accurately
reproduces the measured austral winter methane maximum. At these sites, the
model overestimates the austral summer minimum by ∼1 %,
suggesting that the model slightly underestimates summertime chemical loss.
The model also overestimates boreal summer methane minimums at the NH high-latitude sites (e.g., Summit station),
which is similarly
likely due to a model underestimate in summertime chemical loss. The
model–measurement differences in annual cycles might also be associated
with the temporal and spatial assumptions made in the prescribed methane
emissions inventory. The model fails to capture the annual cycle at a few
locations, notably Pallas-Sammaltunturi in Finland;
Utqiaġvik in Alaska, USA (formerly Barrow);
and Ulaan Uul in Mongolia. The poor correlation between observed and modeled
cycles for this limited set of stations is likely associated with localized
sources and sinks near the measurement sites that are not accounted for in
the large-scale model. Based on interactive methane simulations with the
HadGEM2 chemistry–climate model, Hayman et al. (2014) likewise found
model–measurement discrepancies in the annual cycles at these and other
sites, finding that, in their simulations, the Utqiaġvik (formerly Barrow, as in Fig. 5) and
Pallas-Sammaltunturi sites are strongly influenced by emissions from
wetlands, while the Ulaan Uul site is influenced by other non-wetland
emission sources.
Annual-mean surface methane concentration (ppbv) at 50
locations for both the E2005 simulation and the NOAA ESRL measurements.
Annual cycle of surface methane concentration (ppbv) at
50 locations for both the E2005 simulation and the NOAA ESRL measurements.
The filled circles represent monthly means, and the vertical bars represent
±1 standard deviation. The scale varies by panel. The normalized mean
bias (NMB, %) calculated using monthly means is indicated in the panel titles.
Comparison with satellite retrievals
SCIAMACHY methane columns are available at near-global coverage (Schneising
et al., 2009), providing a means to evaluate model performance in regions
not covered by the more limited NOAA ESRL surface measurement network.
Comparison of modeled methane with SCIAMACHY data provides an independent
post-optimization evaluation. The relative differences in annual
column-averaged methane mixing ratios for E2005 and SCIAMACHY are plotted in
Fig. 6. The SCIAMACHY instrument experienced degraded detector performance
beginning in November 2005 (Schneising et al., 2009); as such, the model
validation using SCIAMACHY-derived methane columns makes use of all
satellite observations available for the period November 2002 to October 2005 (i.e., 3 years of observations for each calendar month). To account for
the altitude sensitivity of the satellite retrievals, the model data were
sampled using the SCIAMACHY averaging kernels and a priori mole fractions
(Schneising et al., 2009). In each model grid cell, the simulated
annual-mean mixing ratio was calculated using only the monthly means
corresponding to the calendar months for which SCIAMACHY has available data.
Relative difference (%) between simulated (E2005) and
SCIAMACHY annual column-averaged methane concentrations. Relative difference
=100×(model-satellite)/satellite. Range =-11.2 % to
+7.1 %.
Ninety-five percent of grid cells with data exhibit a model underestimate in
column-averaged methane, indicating that the total methane source strength
in the model is slightly too weak or the methane sink strength is slightly
too strong. The model underestimate is slight in most grid cells: 83 % of
grid cells with data exhibit an underestimate of <3 %. The
global-mean relative difference in methane columns is a model underestimate
of 1.7 %. Both hemispheres exhibit an identical model underestimate (1.7 %), indicating relative spatial uniformity in model performance. NOAA
ESRL surface measurement stations are largely absent from South America,
Africa, and Australia (Fig. S3). Comparison of the modeled methane columns
with SCIAMACHY retrievals indicates that the model underestimate on these
continents is ∼1 % to 3 % in most locations, which is
equivalent to the underestimates simulated for North America, Europe, and
most of Asia outside of the Tibetan Plateau. Using interactive methane
simulations in the HadGEM2 chemistry–climate model, Hayman et al. (2014)
likewise found that the model underestimated column-averaged methane
concentrations relative to SCIAMACHY observations due to simulated methane
concentrations that decreased too rapidly with increasing altitude. The
HadGEM2 simulations applied an explicit methane loss term to represent
stratospheric methane oxidation (Hayman et al., 2014), while ModelE2 uses
fully coupled dynamic stratospheric chemistry (e.g., Shindell et al., 2006).
The model slightly overestimates annual-mean surface methane at 80 % of
the NOAA ESRL measurement locations and underestimates column-averaged
methane at most locations on the globe. This mis-match could indicate that
the principal chemical sink of methane – reaction with OH – is slightly
too strong in the model outside of the surface layer, or it could indicate
potential issues with the transport mixing rate of methane in the free
troposphere and stratosphere. Future work with other vertically resolved
satellite data products may help unravel the chemical and/or dynamical
causes. Overall, the model shows good agreement with measured methane mixing
ratios, providing confidence in its ability to simulate the principal
chemical and dynamical processes that affect methane in the atmosphere.
Methane lifetime
Further evidence of the model's skill in capturing methane-relevant
processes is found through the close agreement of methane lifetime in the
model with that derived from observations. The chemical lifetime of methane
in E2005 is 10.4±0.1 years, which is nearly identical to the
present-day methane chemical lifetime against OH of 10.6±0.4 years
that was derived from OH estimates based on methyl chloroform observations
(Rigby et al., 2013). The methane chemical lifetime in the model is only
slightly shorter than – but well within the 1 standard deviation range of
– a second observation-based estimate that is likewise based on methyl
chloroform loss to OH: 11.2±1.3 years for 2010 (Prather et al.,
2012). The total lifetime of methane in E2005, taking into account both
chemical loss and the soil sink, is 9.2±0.04 years. This closely
matches the present-day methyl chloroform-based estimates of total methane
lifetime of 9.7±0.4 years (Rigby et al., 2013) and 9.1±0.9 years (Prather et al., 2012), derivation of which makes use of estimates of
the loss rates for the other minor methyl chloroform and methane sinks.
Importantly, the close agreement between the modeled and observation-based
methane lifetimes is a strong indicator that the model appropriately
captures the processes that control atmospheric methane.
Simulated ozone in ModelE2-YIBs
The simulated tropospheric ozone burden for E2005 is 353±1.5 Tg,
which falls well within the range (302–378 Tg, for year 2000) reported for
the 15 global models that participated in the Atmospheric Chemistry and
Climate Model Intercomparison Project (ACCMIP; Young et al., 2013) and is
only 5 % higher than the ACCMIP multi-model mean (337±23 Tg),
indicating good agreement with other global models. The magnitudes of the
simulated annual ozone fluxes are likewise supported by the results of the
ACCMIP study, although only six ACCMIP models report ozone flux magnitudes
for year 2000 (Young et al., 2013). The simulated magnitude of the annual
net flux of ozone from the stratosphere to the troposphere (452±16 Tg yr-1) falls within the ACCMIP range (401–663 Tg yr-1) as does
the simulated magnitude of net chemical production (907±17 Tg yr-1 for E2005; ACCMIP range: 239–939 Tg yr-1). The simulated
annual ozone dry deposition flux (1359±5.7 Tg yr-1) is only 0.7 % higher than the top end of the ACCMIP range (687–1350 Tg yr-1).
Overall, the simulated ozone budget for E2005 shows good agreement with
those reported by the global models that participated in ACCMIP.
NMB (%) of ozone mixing ratios for the E2005 simulation
relative to the Tilmes et al. (2012) ozonesonde climatology.
For each measurement location and pressure, NMB is calculated using monthly
means. Indicated for each pressure is the minimum, maximum, median, and mean
NMB from the full suite of 41 stations.
Validation of the simulated ozone concentrations for E2005 is achieved
through comparison with an ozonesonde climatology (Tilmes et al., 2012) that
provides ozone concentrations at 26 pressures for 41 measurement stations.
The Tilmes et al. (2012) climatology is based on measurements from the
period 1995–2011, while the E2005 simulation is roughly representative of
year 2005. Ozone concentrations may have changed in some regions over the
1995–2011 era (e.g., Cooper et al., 2014); thus, the ozonesonde climatology
is used only to provide validation that the model captures the broad
patterns of the global distribution of ozone at the turn of the century. The
distribution of measurement sites is shown in Fig. S4. Roughly half of the
sites are located in either North America or Europe; the other continents
are poorly represented, although there is significant coverage at remote
tropical sites.
Annual-mean ozone concentration (ppbv) at 41 locations
for four pressures for both the E2005 simulation and the Tilmes et al. (2012) ozonesonde climatology.
Figure 7 plots the annual-mean ozone mixing ratios from the ozonesonde
climatology and simulation E2005, with comparisons shown for four pressures.
The data points are arranged according to the latitudes of the measurement
stations. The simulated ozone data correspond to the grid cells that overlap
the individual measurement stations. In the lower troposphere (800 hPa),
there is better agreement between modeled and measured ozone at sites in the
SH and in the NH tropics than at sites in
the NH midlatitudes. In the NH middle and
high latitudes, the model shows a positive bias relative to observations.
Better agreement between the climatology and the E2005 simulation can be
expected for the less polluted sites. At the more polluted NH midlatitudes, strict agreement cannot be expected between the
17-year climatology and the simulated year 2005 that falls toward the
tail end of the climatological period. Nonetheless, for the most part, both model
and measurements show higher ozone concentrations at 800 hPa in the NH midlatitudes than in the SH.
The NMB of modeled ozone at 800 hPa relative to the climatology ranges from
-17.9 % to +41.4 % for the set of 41 sites (Table 4). All NMB
calculations are based on monthly-mean ozone concentrations. The model
likewise exhibits a positive bias at most NH sites in the
middle troposphere (500 hPa, Fig. 7). At many of the NH
sites, the model exhibits an NMB of smaller magnitude at 200 hPa than at
either 500 or 800 hPa. At 90 hPa, the model underestimates stratospheric
ozone relative to the climatology in the polar regions of both hemispheres.
Conclusions
The results of the optimization process using atmospheric modeling indicate
global annual methane emissions of 140 Tg CH4 yr-1 from wetlands;
this derivation assumes accurate representation of the other methane fluxes
and atmospheric chemical loss in the model. The global annual methane
emissions magnitude from all natural sources is 181 Tg CH4 yr-1.
Overall, the total global annual methane emissions magnitude in E2005 is 532 Tg CH4 yr-1, taking into account the natural flux inventory,
anthropogenic emissions derived from the GAINS integrated assessment model
(Amann et al., 2011), and biomass burning and international shipping
emissions from the RCP8.5 scenario (Riahi et al., 2011). The total emission
magnitude falls well within the range reported by a recent review (Kirschke
et al., 2013). Comparison with multiple observational datasets indicates
close agreement between measured and modeled methane lifetime and
atmospheric distribution. The good model–measurement agreement indicates
that the interactive chemistry scheme in the ModelE2-YIBs global
chemistry–climate model, when forced with the updated natural methane flux
inventory, appropriately represents the principal chemical and physical
processes that affect atmospheric methane, providing confidence in the
model's ability to appropriately capture the methane response to
perturbations in emissions of both methane and other short-lived air
pollutants. The improved methane scheme is currently being applied to
time-slice chemistry–climate simulations to quantify the methane response
and concomitant radiative forcing associated with perturbations in
anthropogenic methane emissions. The gridded, natural methane fluxes
associated with the optimized methane scheme in ModelE2-YIBs are provided in
the Supplement. This dataset can serve as a useful starting
point for optimization of the interactive methane schemes in other
atmospheric models. Starting with a reasonable approximation of prescribed
methane fluxes can reduce the computational power and time needed for
optimization in other models, potentially prompting more widespread use of
interactive methane schemes in global modeling. The optimized methane
inventory developed in this study additionally serves as a useful starting
point for a potential follow-up study aimed at optimization for transient
simulations, in which the prescribed methane emissions evolve over time.
The source code for the site-level YIBs model version 1.0 (Yue and Unger, 2015) is available at https://github.com/YIBS01/YIBS site (last access: 5 August 2015).
The source code for the frozen CMIP5/AR5 version of the GISS ModelE2 (Schmidt et al., 2014) can be
obtained from NASA GISS (https://www.giss.nasa.gov/tools/modelE/, last access: 31 July 2014). Included as supplemental
information are the gridded natural methane fluxes and the numerical model
output used to make the figures. Gridded files of natural methane fluxes associated with the Fung et al. (1991) dataset were obtained from
NASA GISS (https://data.giss.nasa.gov/ch4_fung/, last access: 4 June 2014). Column-averaged methane concentrations from SCIAMACHY (Schneising et al., 2009) were obtained
from the University of Bremen (http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/index.html,
last access: 27 April 2015). Other data used as model input or for
analysis of model output are listed in the references.
The supplement related to this article is available online at: https://doi.org/10.5194/gmd-11-4417-2018-supplement.
KLH and NU designed the study. KLH and YZ performed the model
simulations. KLH analyzed the model output and measurement data. KLH
prepared the manuscript with revisions from all co-authors.
The authors declare that they have no conflict of
interest.
Acknowledgements
This project was supported in part by the facilities and staff of the Yale
University Faculty of Arts and Sciences High Performance Computing Center.
The authors thank Vaishali Naik for providing programming code to read the
pre-processed methane surface measurement data, Chris Heyes and Zbigniew
Klimont for providing access to and assistance with the GAINS-derived
anthropogenic emissions inventory, and Greg Faluvegi for providing guidance
on running interactive methane simulations with
ModelE2.Edited by: Fiona O'Connor
Reviewed by: two anonymous referees
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