JOURNAL OF GEOPHYSICAL RESEARCH
Supporting Information for “Regional scale trends in
column CO dominate over most urban scale trends
over 16 years”
Jacob K. Hedelius
1
,a
, Geoffrey C. Toon
2
,
3
, Rebecca R. Buchholz
4
, Laura T.
Iraci
5
, James R. Podolske
5
, Coleen M. Roehl
3
, Paul O. Wennberg
3
,
6
, Helen
M. Worden
4
, and Debra Wunch
1
1
Department of Physics, University of Toronto, Toronto, Ontario, Canada
2
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
3
Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA
4
Atmospheric Chemistry Observations and Modeling, National Center for Atmospheric Research, Boulder, Colorado, USA
5
NASA Ames Research Center, Mountain View, CA, USA
6
Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, USA
a
Now at Space Dynamics Laboratory, Utah State University, North Logan, Utah, USA
Contents of this file
1. Introduction
2. Map of TransCom regions.
3. Figures of X
CO
trends for additional case study cities.
Corresponding author: Jacob K. Hedelius, Department of Physics, University of Toronto,
Toronto, ON M5S 1A7, CAN. (jacob.hedelius@utoronto.ca)
September 7, 2020, 2:39pm
X - 2
HEDELIUS ET AL.: REGIONAL COLUMN CO TRENDS
4. Urban versus rural trends for additional cities
Introduction
Map of TransCom regions.
Figure S1 is a map of the 23 TransCom regions. There are
eleven predominantly land regions, and eleven predominantly water regions. Antarctica
and Greenland are not optimized. A map with full names is in Gurney et al. (2003).
Figures of X
CO
trends for additional case study urban areas.
Plots of X
CO
behavior for additional cities are included in this section. These plots
are included to aid in distinguishing behavior within a given urban area from a larger
region. These are similar to Fig. 10 in the main text. Populations listed here are from
the Global Human Settlement Urban Centre Database. For convenience we have copied
a modified version of the caption here: Trends of MOPITT X
CO
over a given urban
area. (a) Colored points represent monthly averages of grid cells within the urban area
as noted in (d). Gray heatmap is for monthly averages from all grid cells in full region.
(b) Solid lines are averages of urban and non-urban grid cells. Red fill indicates higher
urban X
CO
, and blue fill indicates higher surrounding region X
CO
. Percentiles of urban
grids compared with those throughout the full region are shown as dots. (c) Gridded
monthly differences correspond to the difference between solid lines in (b). Area monthly
averages are averages within shown urban boundaries compared to the average outside
of urban boundaries, but within 250 km of the center. Annual averages also shown. (d)
Median X
CO
values of the monthly averages. Red star marks the urban center. Purple
triangles mark grid cells that define the urban area (which may be partially obstructed
by the red star). Gray line indicates the urban boundaries. Cyan dots mark other urban
areas with a population of at least 50,000. A Lambert azimuthal equal area projection
September 7, 2020, 2:39pm
HEDELIUS ET AL.: REGIONAL COLUMN CO TRENDS
X - 3
is used. (e) Same as (d), but showing the linear trend within each grid cell from fitting
monthly averages from 2002–2017. (f) Map of population density, gridded to 0
.
1
◦
×
0
.
1
◦
.
References
Gurney, K. R., Law, R. M., Denning, A. S., Rayner, P. J., Baker, D., Bousquet, P.,
. . . Yuen, C.-W. (2003, 4). TransCom 3 CO2 inversion intercomparison: 1. Annual
mean control results and sensitivity to transport and prior flux information.
Tellus
B
,
55
(2), 555–579. Retrieved from
http://www.tellusb.net/index.php/tellusb/
article/view/16728
doi: 10.1034/j.1600-0889.2003.00049.x
September 7, 2020, 2:39pm
X - 4
HEDELIUS ET AL.: REGIONAL COLUMN CO TRENDS
60°S
30°S
0°
30°N
60°N
NAmBo
NAmTm
SAmTr
SAmTm
NAf
SAf
EBo
ETm
TrAs
Aus
Eur
NPTm
WPTr
EPTr
SPTm
NOcn
NAtT
AtTr
SAtT
SOcn
ITr
SITr
Transcom regions
Figure S1.
Map showing the different TransCom regions, labeled with their abbreviations.
The 500 most populated cities are shown as black dots.
100
150
200
MOPITT X
CO
(ppb)
(a)
Guangzhou, Guangdong [CHN]
20°N
22°N
24°N
112°E
114°E
116°E
(d)
20°N
22°N
24°N
112°E
114°E
116°E
(e)
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
0
25
50
75
X
CO
(ppb)
(c)
grid, mthly
grid, yrly
area, mthly
area, yrly
20°N
22°N
24°N
112°E
114°E
116°E
(f)
0
20
40
60
80
n
0
20
40
60
80
100
percentile
(b)
110
120
130
140
median X
CO
(ppb)
2
0
2
X
CO
(ppb yr
1
)
0
1000
2000
3000
4000
5000
Pop. dens. (ppl km
2
)
100
150
200
avg. X
CO
(ppb)
Figure S2.
Guangzhou, China. Population: 40.6 million.
September 7, 2020, 2:39pm
HEDELIUS ET AL.: REGIONAL COLUMN CO TRENDS
X - 5
50
100
150
200
250
MOPITT X
CO
(ppb)
(a)
Jakarta [IDN]
8°S
6°S
4°S
104°E
106°E
108°E
(d)
8°S
6°S
4°S
104°E
106°E
108°E
(e)
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
0
25
50
75
X
CO
(ppb)
(c)
grid, mthly
grid, yrly
area, mthly
area, yrly
8°S
6°S
4°S
104°E
106°E
108°E
(f)
0
50
100
n
0
20
40
60
80
100
percentile
(b)
75
80
85
90
median X
CO
(ppb)
0.5
0.0
0.5
X
CO
(ppb yr
1
)
0
1000
2000
3000
4000
5000
Pop. dens. (ppl km
2
)
100
150
200
avg. X
CO
(ppb)
Figure S3.
Jakarta, Indonesia. Population: 36.3 million.
100
150
200
MOPITT X
CO
(ppb)
(a)
Tokyo [JPN]
34°N
36°N
38°N
138°E
140°E
142°E
(d)
34°N
36°N
38°N
138°E
140°E
142°E
(e)
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
0
20
40
X
CO
(ppb)
(c)
grid, mthly
grid, yrly
area, mthly
area, yrly
34°N
36°N
38°N
138°E
140°E
142°E
(f)
0
50
100
n
0
20
40
60
80
100
percentile
(b)
100
110
120
median X
CO
(ppb)
1
0
1
X
CO
(ppb yr
1
)
0
1000
2000
3000
4000
5000
Pop. dens. (ppl km
2
)
75
100
125
150
175
avg. X
CO
(ppb)
Figure S4.
Tokyo, Japan. Population: 33.0 million.
September 7, 2020, 2:39pm