1
Top-down constraints on 2007 and 2010 South America fire Carbon loss:
2
supplementary information.
3
4
1. MOPITT CO emissions adjoint inversion
5
6
Monthly inverse estimates of CO fluxes are performed at 4°×5° using the NASA Carbon
7
Monitoring System flux tools; these are based on MOPITT CO data [Worden et al.,
8
2010], the GEOS-Chem model and a 4D-Var assimilation approach [Jiang et al., 2014].
9
In this approach, we minimize the cost function of the form,
10
11
𝐽(𝐱) = (𝐅(𝐱) − 𝐲)𝐓 𝐒𝚺−𝟏 (𝐅(𝐱) − 𝐲) + (𝐱 − 𝐱 𝐚 )𝐓 𝐒𝐚−𝟏 (𝐱 − 𝐱 𝐚 )
12
13
where x is the state vector of CO emissions, y is a vector of observed concentrations, and
14
F(x) is the forward model which represents the transport of the CO emissions in the
15
GEOS-Chem model and accounts for the vertical smoothing of the MOPITT retrieval,
16
𝐱 𝐚 is the a priori estimate, and 𝐒𝚺 and 𝐒𝐚 are the observational and a priori error
17
covariance matrices, respectively. The cost function is minimized using the adjoint of
18
GEOS-Chem model in a 4D-Var approach [Henze et al., 2007].
19
20
Total CO emission estimates are partitioned into anthropogenic, biomass burning and
21
biogenic emissions based on the relative contribution of prior emission estimates from
22
each category. We anticipate that CO emission partitioning errors are small, given that
23
biomass burning CO emissions are (a) spatially distinct from anthropogenic (non-biomass
24
burning) CO emission sources, and (b) approximately an order of magnitude greater than
25
biogenic CO emissions during the 2007 and 2010 fire years. To determine CO emissions
26
within the study area we (i) re-grid the CO emission estimates at a 1°×1° resolution and
27
(ii) integrate across grid-points within the study area (25°S – 5°S, 90°W – 30°W).
28
29
2. TES CH4/CO
30
31
To quantify large-scale fire emissions CH4-to-CO ratios [Worden et al., 2013b], we use
32
the TES Lite Products version 7 (tes.jpl.nasa.gov/data/) to determine CH4-to-CO slope
33
(CH4/CO) within the study area (25°S – 5°S, 90°W – 30°W) on a monthly basis. We
34
determine mean column CH4 and CO concentrations and their associated uncertainties
35
based on the Worden et al. [2013b] CH4 & CO measurement selection criteria. We use a
36
weighted total least squares algorithm to quantify the CH4/CO slope and associated
37
uncertainties [Krystek & Anton, 2007].
38
39
To distinguish between the regional fire plume and background air masses within our
40
study area, we derive CH4/CO based on co-located CH4 and CO values where CO >
41
110ppb (i.e., CO cutoff = 110ppb). We examine the sensitivity of selecting a CO cutoff =
42
110ppb by imposing cutoff values ranging from 50ppb to 150ppb. We show the resulting
43
CH4/CO values in Figure S1. The results presented henceforth and in the main text are
44
converted from volumetric CH4/CO [ppm (CH4) ppm-1 (CO)] to mass CH4/CO [g (CH4)
45
g-1 (CO)].
46
47
CH4/CO values where the Pearson correlation is insignificant at a 99% level (pvalue >0.01)
48
are excluded from our analysis. For the 2007 and 2010 fires, at a cut-off of CO = 110ppb
49
CH4/CO values are significant in September 2007 (CH4/CO = 0.0669±0.0083), October
50
2007 (CH4/CO = 0.0936±0.0117) and September 2010 (CH4/CO = 0.1093±0.0116). For a
51
±10 ppb change in the prescribed CO-cutoff value (i.e., 100ppb ≤ CO cutoff ≤ 120ppb,
52
see Figure S1) the CH4/CO value standard deviations are 0.0054, 0.0042 and 0.0092,
53
respectively.
54
55
3. Monte Carlo Bayesian inversion approach
56
57
For each species s and land-cover type b, the fire-season emissions 𝐹𝑠,𝑏 are estimated as
58
follows:
59
60
𝐹𝑠,𝑏 = 𝐴𝑏 𝐶𝐵𝐷𝑏 𝐸𝑠,𝑏
(1)
61
62
where 𝐴𝑏 , 𝐶𝐵𝐷𝑏 and 𝐸𝑠,𝑏 are the burned area, combusted biomass density and emission
63
factor (see main text). We refer to the 18 unknown parameters (i.e., 𝐶𝐵𝐷𝑏 and 𝐸𝑠,𝑏 , for s
64
= [CH4, CO], b = [savannas, forest and agriculture], and y = [2007, 2010]) as parameter
65
vector x:
𝑦
𝑦
66
67
2007
2007
2007
2007
2007
2007
2007
2007
2007
x = [𝐶𝐵𝐷𝑠𝑎𝑣.
, 𝐶𝐵𝐷𝑓𝑜𝑟.
, 𝐶𝐵𝐷𝑎𝑔𝑟𝑖.
, 𝐸𝑠𝑎𝑣.,𝐶𝑂
, 𝐸𝑠𝑎𝑣.,𝐶𝐻4
, 𝐸𝑓𝑜𝑟.,𝐶𝑂
, 𝐸𝑓𝑜𝑟.,𝐶𝐻4
, 𝐸𝑎𝑔𝑟.,𝐶𝑂
, 𝐸𝑎𝑔𝑟.,𝐶𝐻4
,
68
2010
2010
2010
2010
2010
2010
2010
2010
2010
𝐶𝐵𝐷𝑠𝑎𝑣.
, 𝐶𝐵𝐷𝑓𝑜𝑟.
, 𝐶𝐵𝐷𝑎𝑔𝑟𝑖.
, 𝐸𝑠𝑎𝑣.,𝐶𝑂
, 𝐸𝑠𝑎𝑣.,𝐶𝐻4
, 𝐸𝑓𝑜𝑟.,𝐶𝑂
, 𝐸𝑓𝑜𝑟.,𝐶𝐻4
, 𝐸𝑎𝑔𝑟.,𝐶𝑂
, 𝐸𝑎𝑔𝑟.,𝐶𝐻4
] (2)
69
70
According to Bayes’ theorem, the probability of x given the observations O, p(𝐱|𝐎) can
71
be described as:
72
73
p(𝐱|𝐎) ∝ p(𝐎|𝐱) × p(𝐱)
(3)
74
75
where p(𝐎|𝐱) is the likelihood of x given O (or the probability of O given x) and p(𝐱) is
76
the prior probability of 𝐱 (Smith et al., 2013). The observations consist of MOPITT
77
derived CO emissions and TES CH4/CO: specifically we use (a) the mean May-Dec
78
2007, May-Dec 2010 CO emissions, (b) the normalized difference between May-Dec
79
2007 and May-Dec 2010 CO emissions, and (c) TES CH4/CO ratios in 2007 and 2010
80
(see table 1). We assume no covariance between these observational constraints,
81
therefore we define p(𝐎|𝐱) (the likelihood of the parameters given the observations O) as
82
follows:
83
84
p(𝐎|𝐱) = exp (−0.5 ∑𝑁
𝑛=1
(M𝑛 −O𝑛 )2
σ2𝑛
)
(4)
85
86
where for N observations, 𝑂𝑛 and 𝑀𝑛 are the nth observation and corresponding model
87
estimate (based on equation 1 in main text) and 𝜎𝑛2 is the variance of 𝑂𝑛 [e.g., Ziehn et
88
al., 2012].
89
90
Quantifying uncertainties (𝜎𝑛2 ) associated with MOPITT CO fluxes remains a
91
computationally challenging task when using adjoint inversion approach (see section S1)
92
to estimate surface fluxes. We assign a conservative estimate of 20% for the accuracy
93
and 5% for the precision in the CO emissions, consistent with the CO emissions estimates
94
shown in Worden et al. [2013a]. The 5% precision estimate is one of the key
95
uncertainties driving the conclusions in this paper, as the conclusions are insensitive to
96
the CO emissions accuracy. We expect that a 5% precision in the normalized difference
97
between 2010 and 2007 CO emissions is a conservative estimate, as the change in
98
emissions are driven by the observed change in CO: within the study area (25°S – 5°S,
99
90°W – 30°W), the observed change in CO has a <2% precision on the standard error of
100
the mean (2 ppb) for 2007 and 2010 peak fire season values of 147 ppb and 131 ppb
101
respectively (figure 1 in main text). We determine the sensitivity of our results to the
102
prescribed uncertainty estimates (i.e. 20% accuracy, 5% precision) in section 4. TES
103
CH4/CO uncertainty estimates are based on the weighted least-squares CH4/CO slope
104
uncertainty (see section 2). The observations and the corresponding model estimates are
105
summarized in Table 1. We quantify the sensitivity of our approach to each uncertainty
106
estimates in section 4.
107
108
We define the prior probability of x, p(𝐱), as follows:
109
110
p(𝐱) = pEF (𝐱) × pCF (𝐱) × pΔECF (𝐱)
(5)
111
112
where pEF (𝐱) is the prior probability of the emission factors, pCF (𝐱) is the prior
113
probability of the combustion factor (discussed below), and pΔECF (𝐱) is the prior
114
probability of the 2007-to-2010 change in emission and combustion factors. The
115
uncertainty terms in the prior probability of x consist of (a) emission factor uncertainty;
116
(b) combustion factor uncertainty; and (c) the uncertainty in the 2007-to-2010 change in
117
emission and combustion factors.
118
119
We characterize pEF (𝐱) based on the Andreae and Merlet [2001] emission factors for
120
forests, savannas and agriculture: to ensure a positive-definite distribution for all CH4 and
121
CO emission factor priors, we approximate the mean and standard deviation of Andreae
122
and Merlet [2001] CH4 and CO emission factors (in agriculture, forests and savannas) as
123
a log-normal probability distribution:
124
2
125
log(𝐸𝐹(p) ) − log(𝐸𝐹0(𝑝) )
pEF (𝐱) = exp (−0.5 × ∑12
𝑝=1 (
log(1+
𝜎𝐸𝐹
0(𝑝)
)
𝐸𝐹0 (𝑝)
) )
(6)
126
127
where 𝐸𝐹(𝑝) represents the pth emission factor in x (equation 2), and EF0 (𝑝) , 𝜎𝐸𝐹0 (𝑝)
128
represent the corresponding emission factor prior and uncertainty. We use the EF0 (𝑝) and
129
𝜎𝐸𝐹0 (𝑝) values reported under the “Savanna and Grassland”, “Tropical Forests” and
130
“Agricultural Residue” categories by Andreae and Merlet [2001]. We note that a log-
131
normal distribution is preferable to truncating the pEF (𝐱) at zero: fire conditions with low
132
CH4 and CO emission factors become increasingly unlikely as combustion efficiency
133
approaches 1 (i.e. 100% combustion efficiency).
134
135
We impose an upper limit on 𝐶𝐵𝐷𝑏 based on the above-ground carbon density 𝐵𝑏 within
136
burned area 𝐴𝑏 . The combustion factor [CFb units: kg (C combusted) kg-1 (C)] can be
137
expressed as
138
139
CFb =𝐶𝐵𝐷𝑏 /𝐵𝑏 .
(7)
140
141
We use the Saatchi et al. [2011] above-ground biomass density map to determine 𝐵𝑏
142
within each monthly MODIS burned area Ab. Although CFb is naturally limited between
143
0<CFb<1, we note that (a) the Saatchi et al. [2011] biomass map is temporally fixed
144
(circa 2003); (b) Saatchi et al. [2011] biomass density pixel-scale errors are typically 30-
145
50% within the study area; and (c) small but significant carbon losses can also occur from
146
the below-ground biomass pools. Moreover litter pools (not included Saatchi et al.
147
[2011]) typically amount to a smaller but significant carbon stock compared to total
148
biomass in savannas and forest ecosystems [e.g., Ward et al., 1996; Prasad et al., 2001;
149
Malhi et al., 2009]. We therefore allow CFb (i.e. the combustion factor relative to Saatchi
150
et al. [2011] Bb) to range between 0 and 2, and pCF(x) is defined as:
151
152
pCF (𝐱) = 1 if 0 <
𝐵𝐶𝐷𝑏
𝐵𝑏
< 2, otherwise pCF (𝐱) = 0.
(8)
153
154
For the sake of simplicity, we optimize CFb instead of CBDb/𝐵𝑏 (and later multiply CFb
155
by Bb to obtain CBDb). Parameter vector x (equation 1) consists of all unknown
156
parameters (combusted biomass density and emission factors for each land-cover type, in
157
2007 and 2010: based on equations 2 and 7, we convert combusted biomass density to
158
combustion factor, therefore x is now defined as:
159
160
2007
2007
2007
2007
2007
2007
2007
2007
2007
2010
x = [𝐶𝐹𝑠𝑎𝑣.
, 𝐶𝐹𝑓𝑜𝑟.
, 𝐶𝐹𝑎𝑔𝑟𝑖.
, 𝐸𝑠𝑎𝑣.,𝐶𝑂
, 𝐸𝑠𝑎𝑣.,𝐶𝐻4
, 𝐸𝑓𝑜𝑟.,𝐶𝑂
, 𝐸𝑓𝑜𝑟.,𝐶𝐻4
, 𝐸𝑎𝑔𝑟.,𝐶𝑂
, 𝐸𝑎𝑔𝑟.,𝐶𝐻4
, 𝐶𝐹𝑠𝑎𝑣.
,
161
2010
2010
2010
2010
2010
2010
2010
2010
𝐶𝐹𝑓𝑜𝑟.
, 𝐶𝐹𝑎𝑔𝑟𝑖.
, 𝐸𝑠𝑎𝑣.,𝐶𝑂
, 𝐸𝑠𝑎𝑣.,𝐶𝐻4
, 𝐸𝑓𝑜𝑟.,𝐶𝑂
, 𝐸𝑓𝑜𝑟.,𝐶𝐻4
, 𝐸𝑎𝑔𝑟.,𝐶𝑂
, 𝐸𝑎𝑔𝑟.,𝐶𝐻4
]
(9)
162
163
To avoid un-realistic changes in 𝐶𝐹𝑏 and 𝐸𝑠,𝑏 between 2007 and 2010 within each land-
164
cover type b, we define pΔECF (𝐱) as:
165
𝐱𝑝,2007
166
pΔECF (𝐱) = exp (−0.5 ×
∑9𝑝=1 (
log(
𝐱𝑝,2010
log(𝑓)
)
2
) )
(10)
167
168
where 𝑥𝑝,2007 are all 2007 parameters in x, 𝑥𝑝,2010 are their 2010 counterparts (see
169
equation 9), and f denotes the uncertainty in 2007-to-2010 change in xp. Specifically, the
170
pΔECF (𝐱) term dictates a 68% probability that each 2007 parameter is within a factor of f
171
of its 2010 counterpart, and a >99% probability that each 2007 parameter is within a
172
factor of 2f of its 2010 counterpart (for example, if f = 1.5, then there is a 68%
173
probability that
174
of these parameters. However, given the seasonal range of reported combustion factor
175
and efficiency measurements [e.g., Korontzi et al., 2003; Hély et al., 2003], we anticipate
176
that f = 2 (i.e. a 1-sigma probability for a factor of 2 change in CFb and Es,b between 2007
177
and 2010) is a conservative constraint on inter-annual variations in fire characteristics.
1
𝐱𝑝,2007
<𝐱
1.5
𝑝,2010
< 1.5). We are not aware of any inter-annual measurements
178
179
We use a Metropolis Hastings Markov Chain Monte Carlo (MHMCMC) algorithm to
180
draw 2 ×105 samples of x from p(𝐎|𝐱). Markov Chain Monte Carlo algorithms have
181
been widely used to solve ecological parameter optimization problems [Hurtt and
182
Armstrong, 1996, Braswell et al., 2005, Ziehn et al., 2012, amongst others]. The
183
MHMCMC algorithm used here is fully described by Bloom and Williams [2014]. The
184
MHMCMC code is available (in matlab) upon request.
185
186
4. Sensitivity to Uncertainty Estimates
187
188
The posterior probability density function of x, p(𝐱|𝐎), is dependent on the prescribed
189
observation and prior uncertainty estimates reported in section 3. To determine the
190
sensitivity of our MHMCMC results to imposed uncertainty values, we perturb individual
191
observation and prior value uncertainties. We categorize observation and prior
192
uncertainties into five groups (the uncertainty groups are summarized in Table 2). We
193
perform 5 sensitivity tests (S1-S5) by (i) perturbing (increasing) the prescribed
194
uncertainty estimates, and (ii) repeating the MHMCMC optimization of all parameters
195
(x). For test S1 we double the uncertainty of mean CO emissions (from 20% to 40%). For
196
test S2 we double the uncertainty of the normalized difference in CO (from 0.05 to 0.10).
197
For test S3 we double the uncertainty of TES CH4/CO ratios (double the values shown in
198
Table 1). For test S4 we double all emission factor uncertainties reported by Andreae and
199
Merlet [2001]. For test S5 we double the combustion and emission factor 2007-to-2010
200
change uncertainty (from f=2 to f=4 in equation 10).
201
202
Based on 105 MHMCMC samples for each sensitivity test (S1-S5), we re-calculate the
203
probability of each hypothesis (see main text), and compare it against the unperturbed
204
hypothesis probabilities (henceforth S0). The results are shown in Table 3. We find that
205
the hypothesis probability results are most sensitive to the 2007-to-2010 emission and
206
combustion factor change uncertainty (test S5), and least sensitive to the mean CO flux
207
uncertainty (test S1).
208
209
5. Repeat fires
210
211
We examine the locations of (a) 2007-2009 fires and (b) 2010 fires to determine the
212
maximum effect of 2007-2009 fire carbon losses on 2010 fires. We derive the location of
213
2007-2010 Jul-Oct fires at a 1km × 1km resolution (based on the MCD14ML product:
214
https://earthdata.nasa.gov/active-fire-data). We select MCD14ML fire events with a
215
confidence value >95%. We find that there is a 9.19% overlap at a 1km × 1km between
216
2007-2009 fire locations and 2010 fire locations (see Figure 3). We multiply the Saatchi
217
et al. [2011] above-ground biomass map by the 1km × 1km gridded fire locations (i.e.
218
equivalent to assuming a 100% burned area within each grid-cell) to determine potential
219
C losses within each pixel. The upper limit of C loss is the unlikely instance of (a) 100%
220
overlap within each 1km × 1km gricell (b) 0% biomass recovery, and (c) a 100%
221
combustion completeness. Given these upper-limit C losses for 2007-2009 fires, C losses
222
from 2010 fires would be 8.25% lower than 2007 fire C losses. Given that litter and foliar
223
C stocks are likely to partially recover, fires do not necessarily overlap at sub-pixel scale,
224
and combustion completeness is always < 100%, the effect of repeat fires on 2010 C loss
225
is most likely substantially below the 8.25% limit.
226
227
6. OMI NO2
228
229
Fire season (July-October) monthly mean NO2 values for 2006-2011 are shown in Figure
230
S3. Monthly mean NO2 estimates are based on the OMI standard NO2 product [Bucsela
231
et al., 2013; OMNO2, 2013], averaged to a 0.2°× 0.2° grid, excluding any measurements
232
that are affected by the row anomaly [KNMI, 2012] and that contain cloud radiance
233
fractions (CRF) of 80% or higher [Stammes et al., 2008]. The choice of such a high CRF
234
is motivated by the fact that the smoke plumes from biomass burning events are flagged
235
erroneously as cloud contamination by the OMI O2-O2 cloud product, and a smaller CRF
236
threshold would result in discarding otherwise cloud-free biomass burning events. For the
237
data points in Figure S3, the 0.2°×0.2° monthly averages are aggregated further over the
238
study area (5°S-25°S, 30°W-90°W). No correction has been made to account for
239
anthropogenic emissions (e.g., over Manaus, Brazil), because their overall contribution to
240
the total NO2 signal over the Amazon is relatively constant from month to month, and
241
negligible during times of biomass burning.
242
243
7. 2007 and 2010 GOME-2 chlorophyll fluorescence
244
245
We compare satellite-based observations of mean solar-induced fluorescence (SIF)
246
during the 2007 and 2010 months (February-June) preceding the major fire months (July-
247
Oct): we use the 0.5° × 0.5° gridded monthly GOME-2 version 25 SIF product [Joiner et
248
al., 2013] (data available at avdc.gsfc.nasa.gov). On a 0.5° × 0.5° grid within the study
249
area, we determine pixels where SIF observations were available each month: we also
250
evaluate SIF within pixels where the July-October fires were of similar size during both
251
years (0.5 < BA2007/BA2010 < 2). This area accounts for 78% of the total burned area
252
during May-Dec 2007 and 74% of the total burned area during May-December 2010. By
253
selecting these pixels we aim to minimize the effects of fire location and SIF sampling
254
differences between the two years. The selected pixels within the study are shown in
255
figure S4. Based on mean (area weighted) SIF, we find 2010 SIF is 4% lower relative to
256
2007 SIF during the pre-fire months (mean SIF2007 = 1.14 mW/m2/nm/sr and mean
257
SIF2010 = 1.09 mW/m2/nm/sr). In comparison, mean SIF within all consistently sampled
258
SIF pixels was 6% lower (mean SIF2007 = 1.04 mW/m2/nm/sr and mean SIF2010 = 0.98
259
mW/m2/nm/sr) in 2010 relative to 2007. To account for anomalously low sampling of SIF
260
retrievals in April 2007, we repeat the SIF calculation without April SIF values: we find
261
an overall 5%-7% SIF reduction for the February-June (minus April) time-period. In
262
addition to sampling differences between 2007 and 2010, we note that residual cloud and
263
aerosol differences, as well as intra-month vegetation trends, are additional potential
264
sources of bias in our estimate of 2007-to-2010 change in SIF [Joiner et al., 2013].
265
266
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268
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1
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Figure S1. Monthly CH4/CO values (denoted as colors) based on TES mean column CH4
355
and CO retrievals within the study area. The CH4/CO color-scale is shown on the right.
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The “CO cutoff” corresponds to the minimum CO value used to distinguish CH4/CO in
357
fire plumes from background CH4/CO. The dashed line shows the CO cutoff value
358
selected for this study. Inset: map of study region.
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360
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Figure S2. 2007-2009 MCD14ML fire locations (blue) 2010 MCD14ML fire locations
362
(red) and overlap between 2007-2009 and 2010 fire locations (green). The fire locations
363
were gridded at a 1km × 1km resolution. The overlapping locations (green area) account
364
for 9.19% of all 2010 fire locations within the study region.
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366
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Figure S3. Mean monthly OMI NO2 aggregated within the study region for 2006-2011
369
fire seasons. Inset: map of study region.
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371
1
2
372
373
Figure S4. Top left: Map Study region; Top-right: 0.5 × 0.5 degree pixels with (i) SIF
374
observations for every month during Jan-Apr 2007 and Jan-Apr 2010, and (ii)
375
comparably sized burned area during both 2007 and 2010 (0.5 < BA2007/BA2010 < 2);
376
Bottom-left: Mean Jan-Apr 2007 SIF where 0.5 < BA2007/BA2010 < 2; Bottom-right: Mean
377
Jan-Apr 2010 SIF where 0.5 < BA2007/BA2010 < 2.
378
379
380
381
382
383
384
385
Table 1: Observational constraints, associated uncertainties, and equivalent model
386
quantities.
n
Description
Observational constraint (O𝑛 )
Uncertainty
Model (M𝑛 )
(σ𝑛 )
1
Mean May-Dec MOPITT
(MCO2010 + MCO2007 )/2
20%
2007
2010
(𝐹𝐶𝑂
+ 𝐹𝐶𝑂
)/2
CO (MCO) derived
emissions
2
Normalized Difference
between 2007 and 2010
5%)
394
395
396
397
2007
2010
[𝐹𝐶𝑂
+ 𝐹𝐶𝑂
]
2007
2010
[𝐹𝐶𝑂 + 𝐹𝐶𝑂 ]
𝑂𝑐𝑡07
𝑂𝑐𝑡07
𝐹𝐶𝐻4
/𝐹𝐶𝑂
TES CH4/CO mass ratios
CH4/CO Sep 07 = 0.0669
0.0083
*
4
(see section 2)
CH4/CO Oct 07 = 0.0936
0.0117
*
CH4/CO Sep 10 = 0.1093
0.0116
*
agriculture fluxes.
393
2
3
388
392
normalized
derived emissions
*
391
0.05 (i.e.
difference of
387
390
[MCO2010 − MCO2007 ]
[MCO2010 + MCO2007 ]
(May-Dec) MOPITT CO
5
389
2
𝑆𝑒𝑝10
𝑆𝑒𝑝10
𝐹𝐶𝐻4 /𝐹𝐶𝑂
𝑂𝑐𝑡10
𝑂𝑐𝑡10
𝐹𝐶𝐻4
/𝐹𝐶𝑂
Fluxes 𝐹𝐶𝑂 and 𝐹𝐶𝐻4 correspond to the total CO and CH4 fluxes from savanna, forest and
398
Table 2: Sensitivity tests1
Sensitivity test
Quantity
Uncertainty Perturbation
S0
All observations/priors
None
S1
Mean [2007 & 2010) MOPITT CO based CO
Double 𝛔1 (see Table 1)
flux estimate
S2
Normalized MOPITT CO based CO flux
Double 𝛔2 (see Table 1)
difference (between 2010 and 2007)
S3
TES CH4/CO ratios
Double 𝛔3,4,5 (see Table 1)
S4
prior emission factor values
Double σEF0 (p) (see equation 7)
S5
prior constraint on 2007-to-2010 change factor
Double uncertainty (change f=2
in EFs,b and CFb
to f=4 in equation 10)
399
1
400
values.
401
402
403
404
405
406
407
408
409
410
411
412
The sensitivity tests used to quantify the dependence of results to prescribed uncertainty
413
Table 3: Probability of hypotheses H1-H41
Sensitivity H1
H2
H3
H4
H1 & H2 H2 & H3
Test
S0
28%
60%
12%
0%
88%
72%
S1
27%
61%
12%
0%
88%
73%
S2
28%
57%
14%
0%
86%
72%
S3
33%
57%
10%
0%
90%
67%
S4
23%
53%
24%
0%
76%
77%
S5
40%
50%
10%
0%
90%
60%
414
1
415
based on observations & priors with enhanced uncertainty estimates (sensitivity tests S1-
416
S5). Table 2 summarizes the uncertainty perturbations for individual sensitivity tests (S1-
417
S5).
418
419
420
421
The probability of hypotheses H1-H4 based on original uncertainty estimates (S0) and