Two New Applications of Satellite Remote Sensing: Timely Updates to Emission Inventories and Constraints on Ozone Production Randall Martin, Dalhousie and Harvard-Smithsonian Lok Lamsal, Matthew Cooper, Akhila Padmanabhan, Aaron van Donkelaar, Dalhousie U Qiang Zhang, Tsinghua University Chris Sioris, Environment Canada Kaley Walker, University of Toronto Chris Boone, University of Waterloo Peter Bernath, York University Bastien Sauvage, University of Toulouse AGU Fall Meeting 17 December 2010

Bottom-Up Emission Inventories Take Years to Compile Bottom-up Anthropogenic NO x Emission Inventory from Land Sources for 2006 Based on EDGAR (2000), CAC (2005), NEI2005, BRAVO (1999), EMEP (2006), Zhang (2006), scaled to 2006

Changes in Tropospheric NO 2 Column Reflect Changes in NO x Emissions Trend in Tropospheric NO 2 Column over from GOME Richter et al.,

Discrepancy in Satellite NO 2 and Bottom-up NO x Trends for China Does Not Account for Feedback of NO x Emissions on NO x Lifetime Zhang et al., 2007 Satellite NO 2 Column Bottom-up NO x Emissions Relative Change

SCIAMACHY Launched in 2002 onboard Envisat UV-Vis-NIR spectrometer Retrievals include tropospheric NO 2 column (Akhila Padmanabhan) Global coverage in >6 days GEOS-Chem Global Chemical Transport Model Global resolution of 1 o x 1.25 o (Lok Lamsal)

Application of Satellite Observations for Timely Updates to NO x Emission Inventories Use GEOS-Chem to Calculate Local Sensitivity of Changes in Trace Gas Column to Changes in Emissions Walker et al., ACP, 2010 Local sensitivity of column changes to emissions changes Fractional Change in Emissions Fractional Change in Trace Gas Column Lamsal et al., GRL, submitted Apply to regions where anthropogenic emissions dominate (>50%) GEOS-Chem calculation of β for 2006 Insensitive to changes in anthropogenic CO and VOCs

Evaluate Hindcast Inventory Versus Bottom-up Hindcast for 2003 Based on Bottom-up for 2006 and SCIAMACHY NO 2 for Lamsal et al., GRL, submitted HindcastBottom-up

Forecast Inventory for 2009 Based on Bottom-up for 2006 and SCIAMACHY NO 2 for Temporary Dataset Until Bottom-Up Inventory Available Lamsal et al., GRL, submitted 9% increase in global emissions 21% increase in Asian emissions

Models Explain Wave-1 Pattern in Tropospheric Ozone in part by High Ozone Production Efficiency (>100 mol/mol) of Upper Tropospheric NOx OMI How well do Models Simulate Ozone Production Efficiency? Sauvage et al., JGR, 2007 OPE in lower trop: <10 mol/mol [Trainer et al, 1993] OPE in middle trop: 50 mol/mol [Hudman et al., 2004]

Ozone Production Efficiency (OPE) Convective outflow: No HNO 3, some initial O 3, NO x Can estimate OPE using O 3 and HNO 3 concentrations

Testing the Method How does calculating OPE from concentrations compare to direct calculation from production rates? Difference driven by loss of HNO 3 (ie scavenging) OPE from concentrations is an upper limit estimate Using GEOS-Chem: Cooper et al., JGR, submitted Direct Calculation

ACE-FTS Launched in 2004 Solar Occultation Instrument: 15 sunrises & sunsets / day Vertical Resolution <3 km Retrievals include O 3 & HNO 3

Ozone Production Efficiency from ACE-FTS OPE given by slope OPE = 200(+40, -64) mol/mol Uncertainty sources: Slope error = ±7 mol/mol ACE biases = ±10 mol/mol Initial O 3 = ±37 mol/mol Loss of HNO 3 = -50 mol/mol Comparable to tropical mean OPE from GEOS-Chem (140 mol/mol) First estimate of OPE using satellite data OPE from ACE-FTS (11.5 km) r=0.71 Cooper et al., JGR, submitted

Spatial Variation in Ozone Production Efficiency Whole TropicsTropical AtlanticTropical Pacific ACE-FTS OPE (mol/mol) 200(+40,-64)150(+17,-54)250(+20,-54) OPE from GC O 3 outflow (ppbv)25±134±319±2 Number of Points OPE from GEOS-Chem Production Rates (11 km) Cooper et al., JGR, submitted

Two New Applications of Satellite Remote Sensing Timely Updates to NOx Emissions Intentionally simple approach Fills the gap until bottom-up inventories become available Continue developing simulation to relate NO 2 to NO x Emissions Satellite-based Estimate of Ozone Production Efficiency Broadly consistent with current understanding Develop representation of HNO 3 scavenging Higher observations frequency could yield spatially-resolved estimate