Transcript Methods for Incorporating Lightning NOx Emissions in CMAQ Ken Pickering – NASA GSFC, Greenbelt, MD Dale Allen – University of Maryland, College Park, MD Rob.

Methods for Incorporating
Lightning NOx Emissions
in CMAQ
Ken Pickering – NASA GSFC, Greenbelt, MD
Dale Allen – University of Maryland, College Park, MD
Rob Pinder and Tom Pierce – US EPA, Research
Triangle Park, NC
Motivation for Including Lightning NOx in CMAQ
• Production of NO by lightning (LNOx) is an important part of the
tropospheric NOx budget (tropical UT: >70% over broad regions;
summer US: 20-60%), but it is also the most uncertain
component.
• Global annual lightning NO production has been estimated to be
2-8 Tg N/yr (Schumann and Huntrieser, 2007).
• Global CTMs estimate impact of LNOx on surface O3 to be
several ppbv on average.
• In most of the free troposphere O3 production rates are highly
sensitive to NOx mixing ratios. The maximum effectiveness of
ozone as a greenhouse gas is in the UT/LS.
• Comparison of CMAQ tropospheric column NO2 with aircraft
observations shows low bias in model. LNOx source needed.
• Lightning observations from surface networks and satellites are
being used in conjunction with cloud-resolving, regional and
global models in attempts to further reduce the LNOx uncertainty.
NASA GMI CTM
Missing NO2 Aloft
• When paired with
aloft measurements
from NASA INTEX,
CMAQ underpredicts
NO2 above the mixed
layer
• Consistent on all
flights during the
summer of 2004
• On average 1.07
(1015 molecules cm-2)
Pinder et al., 2008
Requirements for Specifying Lightning NO
Production in Regional Chemical
Transport and Climate Models
1)
Flash rates need to be estimated for the times and locations for which
parameterized deep convection is active in the model.
Based on best fit between MM5 convective precipitation and
observed flashes from National Lightning Detection Network (NLDN)
2)
An estimate of average LNOx production per flash
Based on cloud/chemistry model simulations for observed midlatitude
and subtropical thunderstorm events
3)
A method of specifying the effective vertical distribution of LNOx
Based on average profile of flash components from 3-D Lightning Mapping
Array (LMA) data
CMAQ Lightning Parameterization
F = G * αi,j * (preconi,j – threshold), where
precon is the convective precipitation rate from MM5
threshold: A value of precon below which the flash rate is assumed to
equal zero.
G is a global scaling factor chosen so that the domain-averaged MM5
flash rate matches the domain averaged observed flash rate.
αi,j is a local adjustment factor chosen so that the monthly avg modelcalculated flash rate for each grid box equals the observed monthly
avg flash rate at each grid box.
Note: Threshold is chosen so that when averaged over each month and
all 279x240 MM5 grid boxes, the frequency of MM5-calculated
lightning = the frequency of observed (NLDN) lightning. (The
threshold for June 2006 was 0.32 cm hr-1).
Processing of NLDN flash rates
Raw NLDN data sets contain time sequence of CG flashes over the US.
Create Hourly Time Series for Total Flashes
Interpolate monthly average IC/CG ratios onto 279x240 CMAQ grid by using
values from 0.5°x0.5°4-year data set of Boccippio et al. (2001)
After adjusting for detection efficiency, map raw NLDN CG flash rates onto
hourly CMAQ grid. Multiply by (IC/CG +1) to obtain total “observed” flash
rate. Create time series of hourly total flash rate on the 279x240 CMAQ
grid.
Create Monthly Time Series of Total Flashes
Create time series of monthly average NLDN flash rate (CG flashes only) on
the 279x240 CMAQ grid.
Multiply by IC/CG+1 to obtain time series of monthly avg total flash rate
[Fdata(i,j)].
For each month, calculate the fraction of grid boxes with lightning at any
given time
Based on NLDN CG flashes and OTD/LIS total flashes
Boccippio et al., 2001
Boccippio et al., 2001
Determination of adjustment factors
Using hourly precon values from MM5, calculate the monthly
mean nominal flash rate at each CMAQ grid box, where
Fnominal(i,j) = G * Σ (precon(i,j) – threshold) / N. Sum over N the
number of hours with NLDN observations in that month.
Calculate the adjustment factor at each grid box:
α(i,j) = Fdata(i,j) / Fnominal(i,j)
Factors mostly in range 0.3 to 6. Set adjustment factor to 1 at
CMAQ grid boxes that did not have deep convection at any time
during that month.
Using appropriate adjustment factors calculate flash rate
(F = G * αi,j * [preconi,j – threshold]) at individual grid boxes.
August 2006
R = 0.64
Sept. 2006
R = 0.69
August 2006
Sept. 2006
August 2006
Sept. 2006
LNOx Production Per Flash
• Cloud resolved chemistry modeling (DeCaria et al.,
2000; 2005; Ott et al., 2005; 2007) of observed
convective events from field experiments (STERAO,
EULINOX, CRYSTAL-FACE) has indicated a mean of
500 moles N per CG flash in midlatitudes and
subtropics.
• LNOx production per IC flash approximately equal to
that from a CG flash on average.
• These values have been used in other global and
regional simulations over North America (GEOSChem, FLEXPART, REAM) and have provided good
comparisons with NOx observations.
Lightning NO Production Scenarios
Summary of Five Midlatitude and Subtropical Storms
Orville et al., 2002
Means: 500 moles/flash
0.93 ratio
For global rate of 44
flashes/sec, this implies
~9 Tg N/yr
Vertical partitioning of lightning-NO emissions
•
Use vertical distribution of lightning channels observed by 3-D
Lightning Mapping Arrays (LMA) as guidance in distributing LNOx
vertically. LNOx production also assumed to be proportional to
pressure.
•
Place lightning-NO emissions in all model layers from the surfacelayer to the layer that contains the cloud top (predicted by convective
scheme in CMAQ). The fraction of emissions placed into each model
layer depends on the depth of the layer relative to the depth of the
convective cloud.
•
Create look up tables for various cloud depths.
•
Use the look up tables and the cloud-top-layer time series to create a
time series of 3-d lightning-NO emissions.
•
Create a netcdf file of these emissions for desired time period to be
read into CMAQ.
Vertical Distribution of VHF
Sources – Northern Alabama
Lightning Mapping Array
Apr.-Sept. 2003-2005
D. Buechler, NASA/MSFC
Summary and Future Work
• Use of MM5 convective precipitation rates along with local
scaling factors based on NLDN observations yields
realistic lightning flash rates for use in developing 3-D
LNOx emissions for CMAQ that are consistent with model
convection.
• Appropriate temporal and geographic variability in flash
rates has been demonstrated for Summer 2006.
• Next steps:
Assemble 3-D emission data set
Conduct test runs of CMAQ with LNOx source for
comparison with runs without lightning
Comparisons of CMAQ NOx and O3 output with aircraft
and ozonesonde data; CMAQ NO2 with OMI
tropospheric column NO2 from NASA Aura satellite
Evaluate IC/CG ratios using LMA data
OMI Lightning NO2 (LNO2) Analyses
Case Study: June 3, 2005 Maximum NO2 over Iowa
OMI Cloud Fraction
OMI Level 2 trop. NO2 data used to estimate NOx
production per flash:
Back trajectories run at several levels to determine
number of upwind flashes:
2.35 x 106 moles/15,986 flashes = 474 +/- 125 moles
per flash
This value is comparable with estimates from cloudresolved modeling for storms over the US:
500 moles/flash on average
Level 2 OMI Tropospheric NO2
6 moles NO2
+/- 0.617
x 610moles
2.35 1.64
+/- 0.618
x 10
NO2
LNO2 on 0.25 x 0.25 degree grid
Summary and Future Work
• Use of MM5 convective precipitation rates along with local
scaling factors based on NLDN observations yields
realistic lightning flash rates for use in developing 3-D
LNOx emissions for CMAQ that are consistent with model
convection.
• Appropriate temporal and geographic variability in flash
rates has been demonstrated for Summer 2006.
• Next steps:
Assemble 3-D emission data set
Conduct test runs of CMAQ with LNOx source for
comparison with runs without lightning
Comparisons of CMAQ NOx and O3 output with aircraft
and ozonesonde data; CMAQ NO2 with OMI
tropospheric column NO2 from NASA Aura satellite
Evaluate IC/CG ratios using LMA data
Acknowledgments
NASA Modeling, Analysis and Prediction (MAP) and
Aura Validation Programs
NASA Applied Sciences Air Quality Program