Journal of Geophysical Researech – Atmospheres
Supporting Information for
An evaluation of CMAQ NO2 using observed chemistry-meteorology correlations
Monica Harkey, Tracey Holloway, Jacob Oberman, and Erica Scotty
Nelson Institute Center for Sustainability and the Global Environment (SAGE)
University of Wisconsin-Madison, Madison, WI, USA
Contents of this file
Tables S1 – S3
Figures S1 – S16
1
Table S1. P-values for relationships between NO2 and meteorological variables.
AQS
winter
CMAQ- DOMINO CMAQSFC
COL
AQS
9.62e-13
0
0
0
PBLH
1.11e-12
1.82e-12
6.49e-4
0
WINDSP
9.42e-13
0.518
2.81e-11
0
4.78e-12
INSOL
0.053
6.18e-11
0
4.72e-13
0.374
4.93e-11
7.01e-12
4.72e-11
1.67e-11
9.13e-7
1
summer
CMAQ- DOMINO CMAQSFC
COL
1.03e-12 6.51e-12
8.62e-12
2.06e-11
0
0.077
0
0.054
7.67e-11
0
0
RH
0
3.23e-11
2.43e-11
3.09e-11
4.82e-12
PRECIP
0.071
1.09e-11
2.29e-11
4.66e-11
0.029
0
T
4.49e-4
6.95e-11
0
0
1
1
SLP
4.86e-10
1
1
1
2
Table S2. January-February 2007 averaged correlations between AQS, CMAQ, and DOMINO NO2
and meteorological variables over the continental U.S.
AQS
CMAQSFC
DOMINO CMAQCOL
PBLH
-0.238
-0.385
-0.107
-0.109
WINDSP
-0.190
-0.095
-0.073
-0.066
INSOL
-0.140
-0.304
-0.038
-0.086
RH
0.071
0.325
0.089
0.132
PRECIP
-0.009
0.098
0.002
0.014
T
0.082
0.164
0.039
0.106
SLP
0.029
-0.076
0.004
-0.028
3
Table S3. January-February 2007 standardized partial correlation coefficients (left) and p-values
(right) between NO2 and meteorological variables over the continental U.S.
AQS
CMAQ- DOMINO CMAQSFC
COL
AQS
CMAQ- DOMINO
SFC
CMAQCOL
-0.099
-0.111
-0.153
0.002
PBLH
1.141e
-13
0
0
0.334
-0.119
-0.036
0.002
-0.040
WINDSP
0
0
0.344
0
-0.082
-0.185
0.384
-0.062
INSOL
0
0
0
0
-0.059
-0.083
0.097
-0.136
RH
3.348e
-9
0
1.038e-11
1.557e-11
0.001
0.029
-0.009
-0.009
PRECIP
0.938
0
3.581e-6
3.672e-8
-0.002
0.101
-0.031
-0.056
T
0.850
0
0
1.233e-11
-0.074
-0.004
0.108
0.048
SLP
0
1
3.016e-12
1
0.035
0.034
0.076
0.034
r2
4
Figure S1. Winter (left) and summer (right) average NO:NO2 from AQS (top) and CMAQ-SFC
(bottom).
5
Figure S2. Near-surface winter NO (top row), NO2 (middle) and NO:NO2 (bottom) as measured at
Little Rock, Arkansas (AQS) and modeled for the gridpoint containing Little Rock (CMAQ-SFC), all
values from 1 pm Local Standard Time. CMAQ underestimates both NO and NO2, resulting in lower
NO:NO2 ratios than observed.
6
Figure S3. Averaged January and February 2007 NO2 at 1 pm local standard time from CMAQ at the
lowest model layer (a) and with the DOMINO averaging kernel applied to the whole column (b) in
parts per billion volume (ppbv); from AQS at 1 pm LST, in parts per billion volume (ppbv) (c); from
the DOMINO project, in molecules per square centimeter (d); the correlation between CMAQ and
AQS NO2 (e); and the correlation between CMAQ and DOMINO NO2 (f). The average JanuaryFebruary correlation between CMAQ and AQS NO2 is 0.29, the average between CMAQ and
DOMINO NO2 is 0.42.
7
Figure S4. January-February 2007 average boundary layer height (kilometers, top) and correlations
with NO: between AQS NO2 and boundary layer height (b); between CMAQ-SFC NO2 and boundary
layer height (c); between CMAQ-COL NO2 and boundary layer height (d); and between DOMINO
NO2 and boundary layer height (e).
8
Figure S5. January-February 2007 average wind speed (meters per second, top) and correlations with
NO: between AQS NO2 and wind speed 10-meters above ground (b); between CMAQ-SFC NO2 and
10-meter wind speed (c); between CMAQ-COL NO2 with the averaging kernel applied and wind speed
at the altitude of strongest NO2 signal (d); and between DOMINO NO2 and wind speed at the altitude
of strongest NO2 signal (e).
9
Figure S6. January-February 2007 average 2-meter temperature (degrees Celsius, top) and correlations
with NO: between AQS NO2 and temperature 2-meters above ground (b); between CMAQ-SFC NO2
and 2-meter temperature (c); between CMAQ-COL NO2 with the averaging kernel applied and
temperature at the altitude of strongest NO2 signal (d); and between DOMINO NO2 and temperature at
the altitude of strongest NO2 signal (e).
10
Figure S7. January-February 2007 average 2-meter relative humidity (percent, top) and correlations
with NO: between AQS NO2 and relative humidity 2-meters above ground (b); between CMAQ-SFC
NO2 and 2-meter relative humidity (c); between CMAQ-COL NO2 with the averaging kernel applied
and relative humidity at the altitude of strongest NO2 signal (d); and between DOMINO NO2 and
relative humidity at the altitude of strongest NO2 signal (e).
11
Figure S8. In southwestern Colorado in winter, positive correlations between modeled near-surface
NO2 (in ppbv) and boundary layer height (in km, a) are accompanied by decreasing insolation (in
Watts per square meter, b) and temperature (degrees Celsius, c) and increases in precipitation (in
centimeters, d). See January 21st, for example.
12
Figure S9. In southeastern New Mexico in summer, positive correlations between modeled and
measured near-surface NO2 (in ppbv) and boundary layer height (in km, a) are accompanied by higher
temperatures (in Celsius, b) and increased biogenic emissions of NO (in moles per minute, c). See June
13th and August 20th, for example.
13
Figure S10. In northeastern Georgia in winter, positive correlations between column NO2 (molecules
per square centimeter) and wind speed (meters per second, a) occur with corresponding changes in the
boundary layer height (meters, b). See January 30th and December 2nd, for example.
14
Figure S11. An example of a positive NO2 – WINDSP relationship resulting from transport from
source regions: concentrations of modeled and observed near-surface NO2 increase with wind speed in
eastern Wisconsin when winds are southerly (highlighted in red), as these winds transport emissions
from the Chicago area to lakeshore counties in eastern Wisconsin.
15
Figure S12. In Oklahoma City in winter, positive correlations between modeled and observed nearsurface NO2 and surface pressure (millibars, a) are accompanied by lower wind speeds (meters per
second, b). See July 25th for example.
16
Figure S13. In Atlanta GA in winter, the correlation between modeled and observed near-surface NO2
(in ppbv) and relative humidity (percent, a) is negative when temperature (in degrees Celsius, b) and
boundary layer (in meters, c) decrease, such as with the passage of a cold front. See December 24th for
example.
17
Figure S14. An example of negative correlations between modeled and observed near-surface NO2 (in
ppbv) and relative humidity (percent, a) seen in Nashville TN in summer months. Relative humidity
increases with precipitation (in millimeters, b) increases, with the latter lowering near-surface NO2
amounts. See June 3rd and 8th for examples.
18
Figure S15. The summer average percent difference between WRF and NARR boundary layer height.
Overall, WRF overestimates boundary layer height in comparison to NARR.
19
Figure S16. The summer 2007 average percent difference between WRF and NARR insolation. WRF
underestimates insolation in the western U.S. and overestimates insolation in the east and southeast.
20