Using Global and Regional Models to Represent Background Ozone Entering Texas

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Tai, Greg Yarwood ENVIRON International Corporation Meiyun

1 Using Global and Regional Models to Represent Background Ozone Entering Texas AQRP Project Chris Emery, Ed Tai, Greg Yarwood ENVIRON International Corporation Meiyun Lin Princeton University/NOAA GFDL November 14, 2013 Template

2 Background Ozone production, transport, and fate are highly dynamic Multitudes of anthropogenic and natural sources Many spatial and temporal scales EPA requires photochemical modeling to demonstrate attainment of the national ozone standard As local emissions are reduced, uncontrollable background ozone becomes more significant The background must be more accurately characterized In response, regulatory modeling domains are getting bigger Regional models now cover the US and include worldwide contributions estimated by global models Model downscaling via boundary conditions (BCs) 2

3 Background TCEQ uses the CAMx regional photochemical model for research and regulatory photochemical modeling Two popular global models have been routinely coupled to CAMx: (Bey et al., 2001), Harvard University -4 (Emmons et al., 2010), NCAR and others Both employ parameterizations to treat stratospheric ozone A newer global model has gained attention lately (Donner et al., 2011), Princeton University, NOAA GFDL Fully coupled stratospheric-tropospheric chemistry and dynamics (Lin et al., 2012; Naik et al., 2013) 3

4 Objectives Develop boundary condition inputs for CAMx using,, and Use a CAMx modeling database for 2008 Analyze ozone sensitivity in and around Texas to choice of global model Quantitatively compare performance throughout the southern US against available rural ozone measurements Assess ability to provide reasonable boundary conditions for regional downscaling 4

5 -4 Acquired from NCAR Approach Global Modeling hr global fields, 2.8 lat/lon, 28 vertical levels up 40 km GEOS-5 global meteorological analyses Data were mapped to CAMx/CB05 BCs using 2CAMx interface program 5

6 Approach Global Modeling v Run by ENVIRON hr global fields, lat/lon, 47 vertical levels up to 80 km GEOS-5 global meteorological analyses Doubled 2006 Asia NOx inventory (Zhang et al., 2009) Data were mapped to CAMx/CB05 BCs using the GEOS2CAMx interface program 6

7 Approach Global Modeling Run by Princeton hr global fields, ~200x200 km, 48 vertical levels up to 86 km Nudged to NCEP/NCAR reanalysis meteorological analyses RCP8.5 emissions (high scenario from 5th IPCC), interpolated to 2008 Data processed to standard 2 lat/lon grid ENVIRION and Princeton developed a new interface tool to map data to CAMx/CB05 BCs 7

8 Approach CAMx Modeling April-October 2008 regional modeling database Developed independently by Alpine Geophysics Used for several concurrent AQRP modeling projects Includes both ozone (CB05) and particulate matter CAMx run with BCs from each global model CAMx sensitivity test with invariant BCs to provide a simple reference frame Ozone = 30 ppb NOx = 0.1 ppb NOz = 1 ppb (HNO 3, HONO, N 2 O 5 ) CO = 100 ppb VOC = 5 ppbc 8

9 Model Component Approach CAMx Modeling Description Modeling Period April 1 - October 18, 2008 Modeling Domain Vertical Structure Meteorological Model Chemical Mechanism Deposition Emissions Biogenics On Road Mobile Off Road Mobile Shipping Area Source Point Source Wildfire 36/12 km resolution (4 km not used) 30 Vertical Layers WRF CB05 Zhang GloBEIS MOVES EPA NEI EPA NEI EPA NEI TCEQ BlueSky/EPA SMARTFIRE 2 9

10 Performance Evaluation Sites ROM406 CAN407 GTH161 MEV405 GRC474 PET427 PAL190 CHA467 BBE401 BVL130 KNZ184 ALH157 VIN140 SPD111PNF126 CDZ171 CHE185 ESP127 GRS420 CND125 COW137 SND152 CAD150 CVL151 GAS153 ALC188 SUM156 IRL km 36 km CAST NET Ozone Monitors Used for the AQRP Evaluation Southwest South-central Southeast 10

11 Monthly Global Model 6-hr Ozone Statistics Monthly Fractional Bias at Southwestern CASTNET Sites 6-hr Global Models Fractional Error (%) 7 Monthly Fractional Error at Southwestern CASTNET Sites 6-hr Global Models Monthly Fractional Bias at South Central CASTNET Sites 6-hr hr Global Models Fractional Error (%) 7 Monthly Fractional Error at South Central CASTNET Sites 6-hr Global Models Monthly Fractional Bias at Southeastern CASTNET Sites 6-hr Global Models Fractional Error (%) 7 Monthly Fractional Error at Southeastern CASTNET Sites 6-hr Global Models 11

12 Summary Global Model Statistics Similar performance among all global models Very large global model over prediction bias in SC and SE US during warm season (May Oct) Coarse resolution increases ozone production efficiency, reduces ozone chemical sinks Too much ozone over Gulf, convective influences? All 3 global models performed better in the SW US and performed well year-round too high during warm season (lightning NOx) performed best in spring (stratospheric-tropospheric exchange) 12

13 Monthly Global/CAMx 6-hr Ozone Statistics Monthly Fractional Bias at Southwestern CASTNET Sites 6-hr Global and CAMx Models Monthly Fractional Bias at South Central CASTNET Sites 6-hr Global and CAMx Models Fractional Error (%) Fractional Error (%) 7 7 Monthly Fractional Error at Southwestern CASTNET Sites 6-hr Global and CAMx Models Monthly Fractional Error at South Central CASTNET Sites 6-hr Global and CAMx Models Monthly Fractional Bias at Southeastern CASTNET Sites 6-hr Global and CAMx Models Fractional Error (%) 7 Monthly Fractional Error at Southeastern CASTNET Sites 6-hr Global and CAMx Models 13

14 Model Performance Aloft August-Average Houston Ozone Profiles CAMx Global Models 14

15 Model Performance Aloft May-Average Boulder Ozone profiles CAMx Global Models 15

16 Summary CAMx Model Statistics CAMx warm-season bias in SC and SE US better than global models by 10- Fairly insensitive response to the choice of boundary conditions 6-hr bias/error metrics influenced by over predictions of low ozone at night Bias for MDA8 reduced ~ Simple time/space-constant BCs led to only minor improvements Suggests local causes in the CAMx modeling No clearly superior source of boundary conditions 16

17 Summary CAMx Model Statistics CAMx performed best in SW US,, and related CAMx runs under estimated ozone in the spring months Influenced by deep vertical transport from upper troposphere and lower stratosphere Higher terrain elevation and related CAMx run performed better But CAMx has coarse vertical resolution aloft diffusion Simple BC s clearly inferior in SW US Performance aloft impacts surface ozone over the western US, including west Texas was a superior source of BCs for the SW US 17

18 Coastal CAMx Model Performance Sumatra Sabine Pass Galveston Padre Island O3 [ppb] Time Series of 1-Hourly Ozone at Padre Island Observed GEOS Chem Simple BCs /1 7/2 7/3 7/4 7/5 7/6 7/7 7/8 7/9 7/10 7/11 7/12 7/13 7/14 7/15 7/16 7/17 7/18 7/19 7/20 7/21 18

19 Monthly Coastal CAMx 1-hr Model Performance Monthly Fractional Bias at Galveston Monthly Fractional Error at Galveston Monthly Fractional Bias at Sabine Pass Monthly Fractional Error at Sabine Pass Monthly Fractional Bias at Padre Islands NS Monthly Fractional Error at Padre Islands NS Monthly Fractional Bias at Sumatra Monthly Fractional Error at Sumatra 19

20 Summary Coast Site Analysis Ozone performance at coastal sites Routinely measure very low ozone during on-shore flow Often influenced by BCs Minor source impacts between the boundaries and the Texas coastline Over predictions of nearly 10 at two sites during mid-summer (5-10 ppb observed vs. 30 ppb modeled) Practically identical results among all models Missing important ozone destruction mechanism? 20

21 Acknowledgements Harvard University index.html NCAR Princeton/NOAA GFDL EPA CASTNET index.html NOAA ESRL/GMD ftp://ftp.cmdl.noaa.gov/ozwv/ozone/ Valparaiso University houstondata_2008_2011.htm #2008 This research was supported by the State of Texas through the Air Quality Research Program (AQRP) administered by The University of Texas at Austin by means of a grant from the Texas Commission on Environmental Quality (TCEQ), AQRP Project TCEQ has not yet reviewed the final project report and has not fully reviewed the findings presented here 21

22 Monthly CAMx MDA8 Ozone Statistics Monthly Fractional Bias at Southwestern CASTNET Sites MDA8 CAMx Fractional Error (%) Monthly Fractional Error at Southwestern CASTNET Sites MDA8 CAMx Monthly Fractional Bias at South Central CASTNET Sites MDA8 CAMx Fractional Error (%) Monthly Fractional Error at South Central CASTNET Sites MDA8 CAMx Monthly Fractional Bias at Southeastern CASTNET Sites MDA8 CAMx Monthly Fractional Error at Southeastern CASTNET Sites MDA8 CAMx Fractional Error (%) 22

Using Global and Regional Models to Represent Background Ozone Entering Texas

FINAL REPORT Using Global and Regional Models to Represent Background Ozone Entering Texas AQRP Project 12-011 Prepared for: Dr. Elena C. McDonald-Buller Texas Air Quality Research Program The University