1. Improvement in modeled ozone source contributions via sequential assimilation of Aura satellite measurements into global and regional models Min Huang (min.huang@jpl.nasa.gov), K. W. Bowman (JPL), G. R. Carmichael (U Iowa), M. Lee (JPL), D. K. Henze (CU-Boulder), T. Chai (NOAA/ARL) A. J. Weinheimer (NCAR), R. C. Cohen (UC Berkeley) Acknowledgements: *NASA funding (Aura, ARCTAS, AQAST) *ARCTAS science teams *The TES team at JPL *NASA Ames and U Iowa supercomputers AQAST 6 meeting | Houston, TX | Jan 16, 2014 © 2013. All rights reserved.

2. Ozone variability in the Western US is affected by various sources: We seek a means to reduce the uncertainty in the estimates of their contributions 1. Background 2. Objectives/Methods 3. Assim TES 4. Assim OMI 5. Conclusions Why challenging to model ozone & US vs. non-US source contributions? -Anthropogenic emissions trends (“extra-regional” going up, US dropping) not well represented in the inventories -Impacts of some natural sources (e.g., biomass burning) can be episodically strong and their emissions can be highly uncertain -Uncertainties in model transport, chemistry, deposition, etc Liang et al., 2004 “trans-boundary” pollution transport local emissions

3. This study: Improve modeled total ozone and its partitioning by sequentially assimilating Aura measurements into global and regional chemical transport models *WRF v3.5 meteorology *CARB emissions (all sectors, daily varying, Mar 2013) adjoint v34, 2°x2.5° TES L2 ozone profiles OMI NO 2 tropospheric columns (KNMI) 1. Background 2. Objectives/Methods 3. Assim TES 4. Assim OMI 5. Conclusions 3D Var 4D Var top/lateral boundary conditions Surface monitors Sondes Aircraft (DC-8) STEM 12 km By integrating Aura measurements into a multi-scale modeling system, we aim to improve the estimated impacts on ozone in California from: 1) trans-boundary pollutants; and 2) US ozone precursor (i.e., NOx)’s emissions

4. “Extra-regional” pollutants were mixed with local urban pollution and strong fire emissions. Observed strong variability in free troposphere; high ozone and expanded exceedances areas near the surface. Can observations improve model-estimated ozone source contributions? The case we study: Northern California-Central Valley during June 15-30, 2008 (ARCTAS-CARB field campaign) Trinidad Head sondes Period-mean daily max 8h average ozone Singh et al., 2012 1. Background 2. Objectives/Methods 3. Assim TES 4. Assim OMI 5. Conclusions Ozone along DC-8 (<2 km a.g.l.)

5. Improvement of ~6-10 ppb for the entire period Improvement in boundary conditions: GEOS-Chem w/ constraints from TES ozone: “cross validation” with ozonesondes at Trinidad Head, CA 1. Background 2. Objectives/Methods 3. Assim TES 4. Assim OMI 5. Conclusions After- before, 16 days (w/ TES samples) After assimilation, Jun 22-25 episode Airmasses at ~1.5-3 km offshore can impact inland surface ozone later (Huang et al., 2010) Improvement up to >~20 ppb during strong transport event

6. Impact of boundary conditions (GEOS-Chem before/after assimilation) on STEM ozone: Change in absolute mixing ratios near the surface 1. Background 2. Objectives/Methods 3. Assim TES 4. Assim OMI 5. Conclusions Period-mean near-surface (<~2 km a.g.l.) daytime ozone Contributions from non-US sources on modeled near-surface ozone increased by ~2- 6 ppb after the assimilation (vs. ~6-10 ppb in the free troposphere in GEOS-Chem) Some big differences made by the assimilation not seen at the surface sites (or along flight paths) – but perhaps future satellite missions After assimilation After-Before

7. 1. Background 2. Objectives/Methods 3. Assim TES 4. Assim OMI 5. Conclusions Mean: -0.16 Mean: -0.08 Mean: 0.04 Mean: 0.10 Impact of boundary conditions (GEOS-Chem before/after assimilation) on STEM ozone: Fractional bias (unitless): 2x(model-obs)/(model+obs) Period-mean surface (AQS & CASTNET) daily-max 8h average ozone Period-mean near-surface (< 2 km a.g.l.) ozone along DC-8 flights Negative biases in northern Sacramento Valley dropped by >0.2 Positive biases in the Bay area increased by >0.2 Before After

8. STEM NO 2 : Model-Obs along DC-8 flights in ppb Impact of assimilating OMI NO 2 columns in STEM (before/after assimilation) on near-surface (< 2 km a.g.l.) NO 2 along DC-8 flights 1. Background 2. Objectives/Methods 3. Assim TES 4. Assim OMI 5. Conclusions Assimilation of OMI reduced NO 2 mixing ratios along DC-8 by ~10%, and the error by ~8%, mostly occurred in the valley and the Bay area 10 −9 kg/s/m 2 Before After Assimilation of OMI reduced NOx emissions by ~1.7%: -(5-20)% urban areas; >+80% at some fire locations

9. 1. Background 2. Objectives/Methods 3. Assim TES 4. Assim OMI 5. Conclusions Impact of assimilating OMI NO 2 columns on STEM ozone: Change in absolute mixing ratios near the surface Contributions from US emissions on modeled ozone decreased by up to ~5 ppb after the assimilation. This drop in ozone compensated the increases resulted from assimilating TES in GEOS-Chem - We repartitioned the ozone contributions from local and non-local sources. Period-mean near-surface (<~2 km a.g.l.) daytime ozone After assimilation After-Before

10. Mean: -0.08 Mean: -0.11 Mean: 0.10 Mean: 0.07 1. Background 2. Objectives/Methods 3. Assim TES 4. Assim OMI 5. Conclusions Impact of assimilating OMI NO 2 columns on STEM ozone: Fractional bias (unitless): 2x(model-obs)/(model+obs) Period-mean surface (AQS & CASTNET) daily-max 8h average ozone Period-mean “near-surface” (< 2 km a.g.l.) ozone along DC-8 flights Assimilation reduced the positive biases near Sacramento-Fresno regions by 0.1-0.2 Not much improvement near the Bay area (VOC-limited chemical regime affected) Before After

11. 1. Background 2. Objectives/Methods 3. Assim TES 4. Assim OMI 5. Conclusions Conclusions & Future Work Future work: -additional constraints (VOCs, CO, etc) to distinguish the natural and anthropogenic sources -improving assimilation settings -extended studied period *We demonstrated a prototype multi-scale assimilation system that integrates satellite observations across multiple chemical species to assess the role of non-local and local sources of ozone. *This system updates the estimates of contributions from local and non- local sources to ozone, and the local NOx “sector” emissions. 1) Assimilation of TES and OMI in two steps enhanced transported background ozone by ~4 ppb; reduced the US emission contributions by ~2 ppb 2) Assimilation of OMI repartitioned US NOx emissions reduced anthropogenic emissions by 5-20%, increased biomass burning emissions by >80%.

12. Based on monthly-median TES profiles (filtered by quality flag and c-curve) in region within lat lon (approx): 26-37N | 94-106W Dots: Sep data Pres: hPa Sep data TES-observed tropospheric ozone trend/variability in Texas in 2005-2013 What drove the strong inter-annual variability? any relavance to: - climate - change in source contributions - TES sampling strategies and retrieval algorithm

13. Observations No assim Assim TES Assim TES&OMI Backup Statistically, assimilation of “TES&OMI” case resulted in similar total ozone as the “no-assimilation” case, while improved the “high ozone” scenarios/areas and exceedances