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Dylan Millet Harvard University with

Spatial Distribution of Isoprene Emissions from North America: New Constraints from OMI. Dylan Millet Harvard University with D.J. Jacob and K.F. Boersma ( Harvard ), C.L. Heald ( UC Berkeley ), A. Guenther ( NCAR ), T.P. Kurosu and K. Chance ( Harvard-Smithsonian ). thanks to. NASA-ACMAP.

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Dylan Millet Harvard University with

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  1. Spatial Distribution of Isoprene Emissions from North America: New Constraints from OMI Dylan Millet Harvard University with D.J. Jacob and K.F. Boersma (Harvard), C.L. Heald (UC Berkeley), A. Guenther (NCAR), T.P. Kurosu and K. Chance (Harvard-Smithsonian) thanks to NASA-ACMAP NOAA C&GC Postdoctoral Program OMI Science Team INTEX Science Teams American Geophysical Union Fall Meeting 2006San Francisco, CA

  2. VOCs Affect Atmospheric Chemistry and Climate Isoprene Most important non-methane VOC Global emissions ~ methane (but > 104times more reactive) ~ 6x anthropogenic VOC emissions

  3. Mapping Isoprene Emissions from Space ΩHCHO Isoprene a-pinene detection limit propane 100 km Distance downwind VOC source HCHO vertical columns measured by OMI (T.P. Kurosu & K. Chance) OH, h ki, Yi OH, h kHCHO HCHO VOCs Local ΩHCHO-Ei Relationship Palmer et al., JGR (2003,2006).

  4. Testing the Approach: ΩHCHO = SEisoprene+ B What drives variability in column HCHO? Error Characterization Measured HCHO production rate vs. column amount Mean bias Precision PHCHO (1012 molec cm-2 s-1) isoprene 25–31% error (1σ) in HCHO satellite measurements INTEX-A/B ΩHCHO (1016 molec cm-2) clouds primary source of error HCHO Column Comparison ΩHCHO variability over N. America driven by isoprene 25x1015 25x1015 Other VOCs give rise to a relatively stable background ΩHCHO  Not to variability detectable from space URI (DC8) OMI 25x1015 25x1015 Millet et al., JGR (2006). NCAR (DC8) Aircraft

  5. Defining Spatial Distribution of Eisoprene Using OMI HCHO Isoprene emissions from the MEGAN biogenic emission inventory (summer 2006) HCHO columns from OMI satellite instrument (summer 2006) ? test bottom-up inventories against top-down constraints from OMI mismatch in hotspot locations implications for OH, O3, SOA production

  6. Model of Emissions of Gases and Aerosols from Nature Vegetation-specific baseline emission factors Environmental drivers (T, h, LAI, leaf age, …) Guenther et al., ACP (2006) Drive MEGAN with 2 land cover databases G2006 Guenther [2006] (MODIS3) CLM Community Land Model (AVHRR) MEGAN Isoprene Emissions w/ G2006 vegetation MEGAN Isoprene Emissions w/ CLM vegetation 14.6 TgC 12.2 TgC [1013 atomsC cm-2 s-1]

  7. OMI vs. GEOS-Chem with MEGAN Emissions OMI HCHO: June-August 2006 Similarity in broad spatial distribution (r2 = 0.86) GEOS-Chem HCHO: June-August 2006 But important discrepancies regional & landscape scale  emission hotspots [1015 molecules cm-2]

  8. Relating HCHO Columns to Isoprene Emissions GEOS-Chem HCHO Column MEGAN Isoprene Emissions [1015 molecules cm-2] [1013 atomsC cm-2 s-1] Slope ΩHCHO = SEisoprene+ B Uniform ΩHCHO-Eisoprene relationship Variable ΩHCHO-Eisoprene relationship OMI Isoprene Emissions OMI Isoprene Emissions 11.3 TgC 11.1 TgC [1013 atomsC cm-2 s-1]

  9. Spatial Patterns in Isoprene Emissions OMI Isoprene Emissions OMI Isoprene Emissions 11.3 TgC 11.1 TgC MEGAN w/ CLM vegetation Uniform ΩHCHO-Eisoprene relationship Variable ΩHCHO-Eisoprene relationship 12.2 TgC OMI - MEGAN OMI - MEGAN

  10. Spatial Patterns in Isoprene Emissions MEGAN higher than OMI over dominant emission regions OMI – MEGAN Isoprene Emissions June-August, 2006 MEGAN w/ G2006 Land Cover Ozark Plateau Upper South Wisconsin-Minnesota Large sensitivity to surface database used MEGAN w/ CLM Land Cover CLM-driven MEGAN lower than OMI over deep South & Atlantic coast

  11. Why are Emissions Overestimated in Dominant Source Regions? Bottom-up emissions are too high in Ozarks, upper South, MN/WI OMI – MEGAN Isoprene Emissions June-August, 2006 MEGAN w/ G2006 Land Cover Average Bias: Ozarks: 30-70% Upper South: 20-45% Large emissions driven by oak tree cover, high temperatures Broadleaf tree isoprene emissions overestimated? MEGAN w/ CLM Land Cover G2006 Broadleaf Trees

  12. Why Are CLM Emissions Too Low in Deep South? Average Bias: 50 to >100% OMI – MEGAN Isoprene Emissions June-August, 2006 MEGAN w/ G2006 Land Cover Underestimate of broadleaf tree or shrub coverage? CLM – G2006 Broadleaf Tree Cover CLM – Guenther Shrub Cover MEGAN w/ CLM Land Cover Modeled emissions from evergreen trees or crops too low? CLM Fineleaf Evergreens CLM Crops

  13. Conclusions OMI HCHO columns show similar magnitude & broad spatial patterns as state-of-the-art bottom-up emission inventories (R2 = 0.86)  But reveal major discrepancies at the regional scale Bottom-up isoprene emission estimates are 20 to >70% too high in the dominant emission regions  Ozarks, upper South, MN/WI  Overestimate of broadleaf tree emissions? CLM-driven isoprene emission estimates are 50 to >100% too low over the deep South and along the Atlantic coast  Underestimate of broadleaf tree and shrub cover?  Underestimate of fineleaf evergreen or crop emissions?

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