Improvement in modeled ozone source contributions via sequential assimilation of Aura satellite measurements into global and regional models Min Huang 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 © All rights reserved.

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

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

“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., Background 2. Objectives/Methods 3. Assim TES 4. Assim OMI 5. Conclusions Ozone along DC-8 (<2 km a.g.l.)

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 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

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

1. Background 2. Objectives/Methods 3. Assim TES 4. Assim OMI 5. Conclusions Mean: Mean: 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

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

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

Mean: Mean: Mean: 0.10 Mean: 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 Not much improvement near the Bay area (VOC-limited chemical regime affected) Before After

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%.

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

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