Improving techniques for satellite-based constraints on the lightning parameterization in a global chemical transport model

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Coral Barrett
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Logan Harvard School of Engineering and Applied Sciences, Cambridge, MA W.

1 Improving techniques for satellite-based constraints on the lightning parameterization in a global chemical transport model Murray, Lee T., D. J. Jacob, J. A. Logan Harvard School of Engineering and Applied Sciences, Cambridge, MA W. Koshak NASA Marshall Space Flight Center, Huntsville, AL Funding: NASA Graduate Student Researchers Program Fellowship

Global CTMs need Lightning NO x parameterizations LNOx important factor for controlling

In GEOS-Chem, a 3D global Eulerian CTM of tropospheric O3-NOx-VOC-aerosol chemistry driven

of three components: Flash Rate Parameterization Flash-to-NOx Conversion Vertical

2 Global CTMs need Lightning NO x parameterizations LNOx important factor for controlling global tropospheric oxidative capacity [Lawrence et al., 24], but poorly constrained. In GEOS-Chem, a 3D global Eulerian CTM of tropospheric O3-NOx-VOC-aerosol chemistry driven by NASA GMAO GEOS assimilated met fields (v4), the lightning NOx parameterization consists of three components: Flash Rate Parameterization Flash-to-NOx Conversion Vertical Distribution LNOx(x,y,z,t) [Hudman et al., 27] +##$%&'$()* $ [Pickering et al., 1998]!"#$%&'$()* $ [Martin et al., 26]

Existent flash parameterizations are unable to capture

unconstrained flash rate parameterization under the GEOS v4 met

[1992] Matches <25% of observed variance in the Lightning

3 Existent flash parameterizations are unable to capture heterogeneity of LIS/OTD satellite observations Most accurate unconstrained flash rate parameterization under the GEOS v4 met framework is the Cloud Top Height (CTH) method of Price and Rind [1992] Matches <25% of observed variance in the Lightning Imaging Sensor and Optical Transient Detector High Resolution Monthly Climatology v2.2, from NASA GHCC LIS/OTD log fl km-2 a-1 2 CTH OTD (1995-2) LIS ( ) -2 <-4 >5 R=.46 OTD (1995-2) LIS ( ) fl km-2 a-1

Current strategy is to redistribute via scaling factors Lightning must remain linked with convective transport of BL ozone precursors Many CTMs constrain by

<.1..1.2.3.4.1 Histogram of Local Redistribution Scaling Factors 1. 1. 1.< -1-5 5 log scaling factor Factors fall over many orders of magnitude - what is physical meaning?

4 Current strategy is to redistribute via scaling factors Lightning must remain linked with convective transport of BL ozone precursors Many CTMs constrain by determining regional scaling factors to match the parameterized and the observed 11-year flash climatology, redistributing lightning between regions Local Native resolution, e.g., [Sauvage et al., 27] Perfect correlation between param and observations Lightning decoupled from relative convective precursor transport Example July Redistribution Factors Density < Histogram of Local Redistribution Scaling Factors < log scaling factor Factors fall over many orders of magnitude - what is physical meaning? But performed at what spatial scale? Example July Redistribution Factors < < Regional Any size Factors closer to unity, truer to physical param Met-fields drive intraregional variability Decreasing correlation with increasing size Non-physical discontinuities How do we objectively pick regions?

5 Hierarchical clustering oﬀers an objective and datadriven way to select regions Treats every box initially as an individual region, iteratively ﬁnding nearest neighbors by a similarity metric, joining the two most similar clusters, until single cluster found This establishes a dendogram of similarities; where one truncates the tree establishes the number of regions Example Dendogram No Redistribution 1 Region More Regional 2 Regions 3 Regions 6 Regions More Local Native Redistribution 1 Regions 18 Regions

Hierarchical clustering oﬀers an objective and datadriven way to select regions July Clustered Regions Treats

the two most similar clusters, until single cluster found This establishes a dendogram of similarities; where one

Redistribution 1 Region More Regional 2 Regions 3 Regions 6 Regions More Local Native Redistribution h2 1 Regions

6 Hierarchical clustering oﬀers an objective and datadriven way to select regions July Clustered Regions Treats every box initially as an individual region, iteratively ﬁnding nearest neighbors by a similarity metric, joining the two most similar clusters, until single cluster found This establishes a dendogram of similarities; where one truncates the tree establishes the number of regions Further from root, more local h1 Example Dendogram No Redistribution 1 Region More Regional 2 Regions 3 Regions 6 Regions More Local Native Redistribution h2 1 Regions 18 Regions Hierarchical cluster analysis performed comparing each month s mean CTH ﬂash rate for with the LIS/OTD HRMC v2.2 product Similarity matrix deﬁned on distances in (1) space (ECEF coordinates), (2) land/water indices, and both (3) absolute and (4) relative diﬀerences in modeled and observed ﬂashes h3 h4 Closer to root, more continental

7 Hierarchical clustering oﬀers an objective and datadriven way to select regions Treats every box initially as an individual region, iteratively ﬁnding nearest neighbors by a similarity metric, joining the two most similar clusters, until single cluster found This establishes a dendogram of similarities; where one truncates the tree establishes the number of regions July Clustered Regions h1 ~15 regions month-1 Example Dendogram No Redistribution 1 Region More Regional 2 Regions 3 Regions 6 Regions More Local Native Redistribution h2 1 Regions 18 Regions Hierarchical cluster analysis performed comparing each month s mean CTH ﬂash rate for with the LIS/OTD HRMC v2.2 product Similarity matrix deﬁned on distances in (1) space (ECEF coordinates), (2) land/water indices, and both (3) absolute and (4) relative diﬀerences in modeled and observed ﬂashes Two truncation levels chosen for sensitivity simulations h3 ~35 regions month-1 h4

8 Improved spatial heterogeneity in flash densities Performed sensitivity simulations for Jul 24-Aug 25 at 2ºx2.5º resolution, with a 12- month spin-up for each redistribution method (local, regional.h2, regional.h4), no redistribution (base), and no lightning. 1 July Scale Factors Local (Native Redistribution) Regional.h2 Regional.h4 Base (No Redistribution) 1 LIS/OTD July Climatology Log Scale Linear Scale log flashes km -2 min -1 unitless e-4 < Parameterized July 24 Monthly Mean Flash Density flashes km -2 min -1. Correlation of parameterized to observed climatology R : 1. R :.88 R :.8 R :.46

Impact on Tropospheric Composition Altitude [km] 15 1 5 Annual Zonal Mean Lightning NOx Emissions Local Regional.

h4 Base LNOx shifted from tropics to N Midlats in all redistributions Decreases tropical and increases N Midlats UT O3 Total column O3 differences greatest over Amazon and Middle East

9 Impact on Tropospheric Composition Altitude [km] Annual Zonal Mean Lightning NOx Emissions Local Regional.h2 13 molec cm-3 s Regional.h4 Base LNOx shifted from tropics to N Midlats in all redistributions Decreases tropical and increases N Midlats UT O3 Total column O3 differences greatest over Amazon and Middle East All redistributions similar in impact 12 molec cm-3 s ppbv 5-5 DU Absolute Difference in Annual Zonal Mean LNOx Emissions Local-Regional.h2 Local-Regional.h4 Local-Base Absolute Difference in Ox Annual Zonal Mean Profile Concentration Local-Regional.h2 Local-Regional.h4 Local-Base Absolute Difference in Annual Mean Tropospheric Ozone Column Local-Regional.h2 Local-Regional.h4 Local-Base

10 In situ aircraft observations demonstrate improvement in modeled free tropospheric ozone profiles INTEX-NA JJA 24 (Eastern N America) 1 8 Altitude [km] Obs + SD No Lightning Base Regional.h4 Regional.h2 Local O3 [ppbv] Thanks to INTEX-NA / ICARTT teams for data

11 In situ aircraft observations demonstrate improvement in modeled free tropospheric ozone profiles 12 MOZAIC JJA 24/25 (N Hemisphere) 1 Altitude [km] Obs + SD No Lightning Base Regional.h4 Regional.h2 Local O3 [ppbv] Thanks to MOZAIC team for ozone data

12 Larger regions enable us to constrain inter-annual temporal variability of lightning in the tropics Regions now large enough for more robust sampling of the LIS orbital observations (Science Data) product from NASA GHCC An additional inter-annual scaling factor can be determined by 12-18h local sun time observations used to protect against diurnal bias This constraint increases inter-annual variability in the LNOx source, eclipsing that of the biomass burning NOx source from the GFEDv2 inventory [van der Werf et al., 26] Tg N day! GFED2 NOx emissions LNOx local spatial constrained LNOx local spatial + regional temporal constrained 6 Tg N yr!1 J A J O J A J O J A J O J A J O J A J O J A J O J A J O J A J O J A J O

Tropospheric O3 Column Monthly Anomaly SD (1998-25)

13 Large inter-annual variability observed in long-term satellite tropical tropospheric ozone columns Tropospheric O3 Column Monthly Anomaly SD ( ) EPTOMS [Ziemke et al., 26] GEOS-Chem with LIS Temporal Variability Dobson Units 5

14 Constraining the temporal variability of LNO x emissions in GEOS-Chem increases O 3 variability Contribution of different sources to Tropospheric O3 Column Monthly Anomaly SD Original LNOx LNOx with LISinduced Variability Dobson Units 3

Lightning is one of the most important factors driving

Contribution of different sources to Tropospheric O3

Biomass Burning Biomass Burning LNOx + LIS var 5 %

15 Lightning is one of the most important factors driving interannual variability in tropical tropospheric ozone Contribution of different sources to Tropospheric O3 Column Monthly Anomaly SD Relative Contribution Biomass Burning Biomass Burning LNOx + LIS var 5 % total variability 1 LNOx with LISinduced Variability 3 Dobson Units

Data Assimilation for Tropospheric CO. Avelino F. Arellano, Jr. Atmospheric Chemistry Division National Center for Atmospheric Research

Data Assimilation for Tropospheric CO Avelino F. Arellano, Jr. Atmospheric Chemistry Division National Center for Atmospheric Research Caveat: Illustrative rather than quantitative, applied rather than