Interannual variability in biogenic emissions driven by dynamic vegetation conditions

Daniel Cohan (PI), Adetutu Aghedo, Erin Chavez-Figueroa, and Ben Lash, Rice UniversityLoretta Mickley and Lei Zhu, Harvard UniversityBryan Duncan, NASAChristine Wiedinmyer and Alex Guenther, NCARAgency partners: TCEQ and CARB

Motivation and Objective• Biogenic emissions strongly impact air quality and its

responsiveness to control measures• Regulatory modeling often uses static, outdated

vegetation conditions• Could dynamic vegetation conditions influence air

quality decision making?

OBJECTIVE: Explore how interannual variability in biogenic emissions is driven by changing meteorology and vegetation conditions

Δ

Biogenics Model(MEGAN)

Δ Δ isoprene

HCHO

OMI (& GOME-2)MODIS

Leaf Area Index (LAI)

Interannual variability in OMI HCHO

Duncan et al., GRL 2009

2007drought

Monthly CA-statewide biogenic reactive organic gas (ROG) emissions (essentially BVOC):

Meteorology is held constant at 2005 levels and 8-day LAI (MODIS) is allowed to change from year-to-year

**** Data is preliminary ****

Average July LAI (from 8-day MODIS)

Slide from Jeremy Avise, CARB

Influence of ΔLAI on MEGAN BVOC

ΔLAI and ΔMeteorology drive ΔBVOC

Gulden et al., JGR, 2007

Progress to Date

• Analysis of interannual variability in MODIS LAI and NARR reanalysis meteorology fields– Processed LAI data obtained from Myneni group (Boston U.)

• MEGAN simulations of ΔBVOC due to ΔLAI– Will extend to variability in meteorology

• Correlations of isoprene with temperature and rainfall• Correlations of OMI HCHO data with meteorology and

EVI vegetation index– Extension to GOME-2 HCHO and to LAI in progress

Biome Types and mean August LAI in MODISLegend2004lndcvr.hdfValue

Water

Grasses/Cereal Crops

Shrubs

Broadleaf Crops

Savannah

Evergreen Broadleaf Forest

Deciduous Broadleaf Forest

Evergreen Needleleaf Forest

Deciduous Needleleaf Forest

Non-Vegetated

Urban

LAIm2 per m2

Biome Types used by MODIS for LAI determination (2004)

Data from R. Myneni group, Boston U.

(Std dev/Mean) ≈ 0.1 for MODIS LAI in August over most forested regions

August 2005: Cool, wet, high LAI over Southeast

Drought Data from NOAA at http://www.ncdc.noaa.gov/temp-and-precip/drought/historical-palmers.php

June 2007: Dry, hot, low LAI over SE US; Cool, wet, high LAI over Texas

Drought Data from NOAA at http://www.ncdc.noaa.gov/temp-and-precip/drought/historical-palmers.php

August 2011: Extreme drought, low LAI over Texas, parts of Southeast

Drought Data from NOAA at http://www.ncdc.noaa.gov/temp-and-precip/drought/historical-palmers.php

LAI ____ Temperature _____

90W to 78W and 32N to 36N (SE US)

LAI (MODIS) and Temperature (NARR) anomalies over Southeast US

Surface isoprene highly correlated with T, but shifts lower in drought year

2007: drought

2008: typical

15

OMI HCHO Slant Column Density during the growing season

For Background correction

1 High HCHO in the SE U.S.2 Wild fires3 Some anthropogenic sources: Houston

16

OMI HCHO Slant Column Density during the winter

Human Activity: in circle High albedo from Snow: High values in the North However, in Texas, especially Houston, HCHO is very low

1

1 From Nicole Downey

Drill Rigs & Wells

17

Temperature drives HCHO levels

Three methods for variable selection: all show temperature is driving factor

(1) (2)

(3)

(1)

HCHO and Temperature anomalies

HCHO correlated with T in growing season, but not in winter

19

HCHO densities turn-over at high temperatureHCHO: OMI, Daily, 0.5 by 0.5 degreeT: 2m above the ground, GOES-5, Daily, 0.5 by 0.5 degree

Then, HCHO was binned by T in a 3 K step-wise:

19

285 1543 3965 4037 1575 631

181

HCHOColumn Density

Turn-over~ 34˚C

20

HCHO and EVI anomalies in Houston, 2005 - 2008

R2=0.003

When seasonal trends are removed, basically HCHO and EVI are independent from each other.

Short-term Next Steps• Correlations among meteorology, LAI, satellite

HCHO, and ground-level isoprene– How to quantify LAI-influencing meteorology, drought?– OMI and GOME-2 HCHO observations jointly?

• MEGAN model predictions of BVOC response to Δmeteorology & LAI, separately and jointly– How to generate or obtain multiple summers of

MEGAN-ready (WRF?) meteorology data?

Long-term Modeling Plan• Model multiple pathways by which variable

vegetation and soil influence air quality– ΔVegetation ΔBVOC (MEGAN)– ΔVegetation ΔDeposition of OVOC & O3 (Karl et al., 2010)

– ΔFertilizer, rain, etc. ΔSoil NOx (Hudman et al. scheme)

• Extend to HONO emissions (Su et al., Science, 2011)

• Compare with satellite and ground observations of HCHO, NO2, and isoprene

• Model the influences on ozone and PM and their sensitivities to emissions

• Influence of climate change?

Hudman linked ΔSoil NO with ΔOMI NO2; 2x soil NO of previous models

Hudman et al., ACP 2010

Karl et al.: Faster than expected vegetative uptake of OVOC; Function of LAI

Karl et al., Science, 2010