Final Report: Characterizing International Emissions and ......Using GEOS-Chem PREPARED UNDER A CONTRACT FROM THE TEXAS COMMISSION ON ENVIRONMENTAL QUALITY The preparation of this

Work Order No. 582-19-92775-04 Contract No. 582-19-90500

Tracking No. 2019-12 Task 6

Prepared for:

Texas Commission on Environmental Quality 12100 Park 35 Circle MC 164 Austin, TX 78753 Prepared by:

Ramboll US Corporation 7250 Redwood Blvd., Suite 105 Novato, California 94945 August 25, 2019

Final Report: Characterizing International Emissions and Background Concentrations Using GEOS-Chem

PREPARED UNDER A CONTRACT FROM THE TEXAS COMMISSION ON ENVIRONMENTAL QUALITY The preparation of this document was financed through a contract from the State of Texas through the Texas Commission on Environmental Quality. The content, findings, opinions and conclusions are the work of the author(s) and do not necessarily represent findings, opinions or conclusions of the TCEQ.

Ramboll 7250 Redwood Boulevard Suite 105 Novato, CA 94945 USA T +1 415 899 0700 https://ramboll.com

Final Report: Characterizing International Emissions and Background Concentrations Using GEOS-Chem

Ramboll - Final Report: Characterizing International Emissions and Background Concentrations Using GEOS-Chem

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CONTENTS

Acronyms and Abbrevations iv

INTRODUCTION 1

Global Model Configuration 2 2.1 GEOS-Chem Version and Setup 2 2.2 Emissions 3 2.2.1 Emission Inventories 3 2.2.2 Emission Projections 5

Measurement Data for Model Performance Evaluation 8 3.1 Global Observation Data Sources 8 3.1.1 Overall Summary 8 3.1.2 Global Observation Dataset Descriptions 10 3.2 Model Performance Evaluation Metrics 18 3.3 Recommended Model Performance Evaluation Configuration for TCEQ

2012 and 2016 GEOS-Chem Modeling Runs 19 3.4 Tools for Global Model Performance Evaluation 21 3.4.1 Surface Observation Tool: evalwdcrg 21 3.4.2 Vertical Ozonesondes Tool: evalwoudc 22

GEOS-Chem 2012 and 2020 Modeling Runs 23 4.1 GEOS-Chem Model Setup 23 4.1.1 2012 Scaling Factors 24 4.1.2 2020 Scaling Factors 24 4.1.3 Emission Summaries 25 4.2 GEOS-Chem Modeling Results 26 4.2.1 Spatial Ozone and PM2.5 Plots for the 2012 simulations 26 4.2.2 Spatial Ozone and PM2.5 Plots for the 2020 simulations 27

Conclusions 34

References 35

Appendices

Appendix A. WDCRG Site Recommendation Appendix B. WOUDC Ozone Sondes Station Recommendation

Table of Figures Figure 2-1. Emissions of SO2 and NOx (top) and greenhouse gas (bottom) across the

RCPs. Source: van Vuuren et.al. 2011. 7 Figure 2-2. Five World Regions in the representative concentration pathway. 7


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Figure 3-1. World Data Centre for Reactive Gases (WDCRG) Sites (top left=O3, top right=NOx, bottom left=SO2, bottom right=VOC). 10

Figure 3-2. World Ozone and Ultraviolet Radiation Data Centre (WOUDC) sites. 11 Figure 3-3. European Monitoring and Evaluation Programme (EMEP) sites. 12 Figure 3-4. Aerosol, Clouds, and Trace Gases Research Infrastructure (ACTRiS) sites. 13 Figure 3-5. Tropospheric Emission Spectrometer (TES) Global Survey example. 14 Figure 3-6. Tropospheric Ozone Assessment Report (TOAR) sites. 15 Figure 3-7. EPA AirNow Department of State (DOS) sites. 16 Figure 3-8. IAGOS network. 17 Figure 3-9. Tropospheric Ozone LIDAR Network (TOLNET) sites. 17 Figure 3-10. Example Q-Q plot comparing GEOS-Chem modeled ozone with ozone

observations from one WDCRG measurement site. 21 Figure 3-11. Example timeseries plot comparing GEOS-Chem modeled ozone with

ozone observations from one WDCRG measurement site. 21 Figure 3-12. Example WOUDC output comparing modeled and observed ozone

concentrations over time. 22 Figure 3-13. Example WOUDC output showing a time-averaged comparison of

modeled and observed vertical ozone concentrations. 22 Figure 4-1. Quarterly-average surface ozone mixing ratios for 2012 basecase

simulation (left), 2012 ZROW simulation (middle), and the difference between the two simulations (right). 28

Figure 4-2. Quarterly-maximum surface ozone mixing ratios for 2012 basecase simulation (left), 2012 ZROW simulation (middle), and the difference between the two simulations (right). 29

Figure 4-3. Quarterly-average surface PM2.5 mixing ratios for 2012 basecase simulation (left), 2012 ZROW simulation (middle), and the difference between the two simulations (right). 30

Figure 4-4. Quarterly-average surface ozone mixing ratios for 2020 basecase simulation (left), 2020 ZROW simulation (middle), and the difference between the two simulations (right). 31

Figure 4-5. Quarterly-maximum surface ozone mixing ratios for 2020 basecase simulation (left), 2020 ZROW simulation (middle), and the difference between the two simulations (right). 32

Figure 4-6. Quarterly-average surface PM2.5 mixing ratios for 2020 basecase simulation (left), 2020 ZROW simulation (middle), and the difference between the two simulations (right). 33

Table of Tables Table 2-1. Recommended GEOS-Chem model configurations. 2 Table 2-2. GEOS-Chem meteorology options. 3 Table 2-3. GEOS-Chem emission inventories recommendations.* 4


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Table 2-4. GEOS-Chem Emission inventories not recommended. 5

Table 3-1. Available observation datasets. 9 Table 3-2. Benchmarks for Model Performance Metrics (adapted from Table 4 in

Emery et al., 2016). 18 Table 3-3. Recommended Datasets and Performance Metrics for TCEQ 2012 and

2016 GEOS-Chem MPE. 20 Table 4-1. 2012 and 2020 Basecase and ZROW GEOS-Chem Emission Inventories 23 Table 4-2. 2012 Anthropogenic Emission Scaling Factors. 24 Table 4-3. 2020 Anthropogenic Emission Scaling Factors. 24 Table 4-4. Anthropogenic Emissions by World Region in 2012. 25 Table 4-5. Anthropogenic Emissions by World Region in 2020 25 Table 4-6. Change in Anthropogenic Emissions from 2012 to 2020 (% change:

[2020-2012]/2012) 26


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ACRONYMS AND ABBREVATIONS ACTRiS ……… Aerosol, Clouds, and Trace Gases Research Infrastructure AMAP…………… Arctic Monitoring and Assessment Programme ASIA …………… Representative Concentration Pathway World Region representing China, India, and the

rest of Asia. AEIC …………… Aviation Emissions Inventory Code APEI …………… Environment and Climate Change Canada Air Pollutant Emission Inventory CASTNET……… Clean Air Status and Trends Network CEDS …………… Community Emission Data System CMDL …………… National Oceanic and Atmospheric Administration Climate Monitoring and Diagnostics

Laboratory CONUS ………… Continental United States ECCC …………… Environment and Climate Change Canada EMEP …………… European Monitoring and Evaluation Programme evalwdcrg …… Tool that compares surface observations from WDCRG network to GEOS-Chem output evalwoudc …… Tool that compares ozonesonde measurements from WOUDC network to GEOS-Chem

output FINN …………… Fire Inventory from NCAR GAW …………… Global Atmospheric Watch Programme GEOS …………… Goddard Earth Observing System GEIA …………… Global Emissions Initiative GEOS-Chem … Goddard Earth Observing System Chemistry Global Chemical Transport Model GEOS-FP ……… GEOS Forward Processing Operational Dataset GFED4 ………… Global Fire Emissions Database, Version 4 IAGOS ………… In-service Aircraft for Global Observing System IPCC …………… Intergovernmental Panel on Climate Change LAM ……………… Representative Concentration Pathway World Region representing Latin America

countries. MAF ……………… Representative Concentration Pathway World Region representing Middle East and

African countries. MDA8 …………… Maximum Daily 8-hour Average MEIC …………… Multi-resolution Emission Inventory for China


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MPE ……………… Model Performance Evaluation MERRA-2 ………Modern-Era Retrospective analysis for Research and Applications, Version 2 Reanalysis

Dataset NEI ……………… National Emission Inventory NMB ………………Normalized Mean Bias NME ……………… Normalized Mean Error OECD90 ……… Organization for Economic Co-operation and Development 90 Countries Representative

Concentration Pathway World Region OMI ………………Ozone Monitoring Instrument QFED …………… Quick Fire Emissions Dataset Q-Q ……………… Quantile-Quantile REF ……………… Countries in Reforming Economies Representative Concentration Pathway World Region RCP ……………… Representative Concentration Pathway TES ……………… Tropospheric Emission Spectrometer TOAR …………… Tropospheric Ozone Assessment Report TOLNET …………Tropospheric Ozone LIDAR Network WDCRG …………World Data Centre for Reactive Gases WMO …………… World Meteorological Organisation WOUDC …………World Ozone and Ultraviolet Radiation Data Centre ZROW ……………Zero-Out-The-Rest-Of-World


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INTRODUCTION The Texas Commission on Environmental Quality (TCEQ) uses the Goddard Earth Observing System (GEOS)-Chemistry global chemical transport model (GEOS-Chem) to derive boundary conditions for the Comprehensive Air Quality Model with Extensions (CAMx) regional photochemical model. Since the TCEQ’s modeling domain extends beyond Mexico and Canada, the boundary conditions mostly represent the transport of pollution due to anthropogenic and natural sources that are located outside North America. Understanding GEOS-Chem assumptions and accuracy can inform the level of confidence on the boundary conditions and by extension the estimates of international emissions and background contribution to future design values at Texas monitors.

TCEQ plans to use GEOS-Chem output for modeling years 2012, 2016, 2020, and 2028. This project has several main objectives, which are designed to help TCEQ complete upcoming regulatory modeling activities. These objectives are as follows:

1. GEOS-Chem Configuration: Provide technical assistance transitioning to the latest version of GEOS-Chem and supply GEOS-Chem emission inventory recommendations for modeling years 2012 and 2016.

2. GEOS-Chem Model Performance Evaluation: Compile global observation datasets and develop accompanying tools to enhance GEOS-Chem global model performance evaluation.

3. Utility to extract CAMx-ready IC/BC/TC files from GEOS-Chem: Develop tool to extract and convert GEOS-Chem netCDF output into boundary conditions for regional photochemical grid models, including CAMx and CMAQ.

4. Conduct GEOS-Chem Modeling: Perform four (4) GEOS-Chem modeling simulations, including 2012 basecase, 2012 zero-out-the-rest-of-world (ZROW), 2020 basecase, and 2020 ZROW. The ZROW runs exclude all anthropogenic emissions outside of the United States.

GEOS-Chem contains many emission inventories built into the model that vary by pollutant, source category, and regional coverage. Available emission inventories vary between versions of GEOS-Chem, and not all emission components are automatically projected or available for a modeling year of interest. Typically, scale factors are applied to emission inventories to scale baseline emissions to future years (e.g., 2020 and 2028). Section 2 of this report outlines available emission inventories in GEOS-Chem and provides global and regional emission inventory recommendations for the 2012, 2016, 2020, and 2028 modeling.

The validity of estimates of international emissions and CAMx boundary conditions can be assessed through comprehensive model performance evaluation. TCEQ routinely conducts model performance evaluation (MPE) against observations within the continental United States (US). Evaluating GEOS-Chem model performance outside the US may build confidence in the international contributions obtained from the model and also may help identify model improvements that are needed. Section 3 of this report identifies available global observational datasets from a variety of networks, including surface monitoring sites, vertical sondes, profilers, and satellites.

Section 4 of this report describes the 2012 and 2020 basecase and ZROW GEOS-Chem modeling runs performed by Ramboll based on the recommendations described in Section 2. The model output for these runs are converted into CAMx version 6.5 boundary condition, initial condition, and top condition files using the geos2aqm tool. The geos2aqm user manual is presented in an accompanying report.


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GLOBAL MODEL CONFIGURATION

2.1 GEOS-Chem Version and Setup We recommend running GEOS-Chem version 12.2.0 (release date: February 19, 2019) with its standard chemical mechanism configuration, which includes detailed tropospheric and stratospheric chemistry (UCX mechanism) along with complex SOA chemistry. There are multiple meteorology options in GEOS-Chem, but only the GEOS Forward Processing (GEOS-FP) operational dataset and MERRA-2 reanalysis dataset are recommended1. The GEOS-FP meteorology is not available for the first part of 2012. If TCEQ requires consistency between 2012 and 2016 simulations, we recommend using the MERRA-2 meteorology for both modeling years.

Table 2-1 presents our recommended GEOS-Chem model configuration and Table 2-2 lists the meteorology options.

Table 2-1. Recommended GEOS-Chem model configurations.

Science Options GEOS-Chem Version Version 12.2.0 Vertical Grid Mesh 72 Layers Chemistry mechanism GEOS-Chem standard chemistry with complex SOA option2. Horizontal Grids 2x2.5 degree (Nx, Ny = 144, 91) Initial Conditions 6-month spin-up; starting from provided initial conditions for standard

chemistry Meteorology 2012 and 2016 MERRA-2 (or GEOS-FP) meteorology Photolysis mechanism Default (FAST-J) Advection Scheme Default (TPCORE) Cloud convection scheme

On / Relaxed Arakawa-Schubert

Planetary Boundary Layer (PBL) mixing

On / non-local scheme implemented by Lin and McElroy

Dry deposition scheme Default (Wesely) Chemistry Solver Default (FLEXCHEM) Parallelization Open Multi-Processing (OMP)

1 http://wiki.seas.harvard.edu/geos-chem/index.php/Overview_of_GMAO_met_data_products#Important_notice_from_NASA.2FGMAO 2 We recommend turning off semivolatile primary organic aerosol (POA) chemistry and isoprene SOA reactions via the volatility-based scheme (VBS) [Pye et al., 2010] to avoid risk of double-counting in the complex SOA chemistry scheme. For more information, see http://maraisresearchgroup.co.uk/Publications/GC-v11-02-SOA-options.pdf

http://wiki.seas.harvard.edu/geos-chem/index.php/Overview_of_GMAO_met_data_products#Important_notice_from_NASA.2FGMAO

http://maraisresearchgroup.co.uk/Publications/GC-v11-02-SOA-options.pdf


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Table 2-2. GEOS-Chem meteorology options.

MERRA-2 GEOS-FP

Data product type Reanalysis Operational

Native vertical grid 72 hybrid 72 hybrid

Native horizontal grid 0.5° x 0.625° 0.25° x 0.3125°

Native file format netCDF4 netCDF4

Regridded to

0.5° x 0.625° global 0.25° x 0.3125° nested 0.5° x 0.625° nested 2° x 2.5°

2° x 2.5° 4° x 5° 4° x 5°

Temporal coverage 1979 - present Mid-2012 - present

2.2 Emissions 2.2.1 Emission Inventories

GEOS-Chem contains multiple global and regional emission inventories that vary by resolution, pollutant, source category, regional coverage, and time period. A complete listing of available inventories is provided on the GEOS-Chem wiki page [http://wiki.seas.harvard.edu/geos-chem/index.php/HEMCO_data_directories]. If the user-defined model year (e.g., 2016) falls outside of the periods available in the emissions database (i.e., pre-1980 or post-2014), GEOS-Chem automatically uses emissions from the closest available year. We recommend using the most recent global and regional emission inventories and developing projection factors for each world region as needed.

Table 2-3 lists the global and regional emissions inventories available in GEOS-Chem that we recommend using for the 2012 and 2016 simulations. Regional anthropogenic inventories, when turned on, will overwrite global inventories where the data is available. We recommend using the 2011 National Emission Inventory (NEI) emission inventory3, including domestic shipping, in the continental US (CONUS). Outside of the CONUS, we recommend using the Environment and Climate Change Canada (ECCC) Air Pollutant Emission Inventory (APEI) 4 in Canada and the Multi-resolution Emission Inventory for China (MEIC) (Zheng et al., 2018)5. For the rest of the world, we recommend using the Community Emission Data System (CEDS; Hoesly et al., 2018) inventory, which is the default global anthropogenic inventory in GEOS-Chem. GEOS-Chem uses CEDS to fill gaps in the regional inventories. For example, CEDS is used for Hawaii and Alaska because the US NEI inventory in GEOS-Chem does not include these states. Canada’s APEI does not include VOC so CEDS VOC emissions are

3 https://www.epa.gov/sites/production/files/2015-10/documents/nei2011v2_tsd_14aug2015.pdf 4 https://www.canada.ca/en/environment-climate-change/services/pollutants/air-emissions-inventory-overview.html 5 MEIC will be included in version 12.3.0. However, it can be made available for use in previous versions of GEOS-Chem if given a permission by the MEIC developer team.

http://wiki.seas.harvard.edu/geos-chem/index.php/Overview_of_GMAO_met_data_products#MERRA-2_reanalysis

http://wiki.seas.harvard.edu/geos-chem/index.php/Overview_of_GMAO_met_data_products#GEOS-FP

http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-Chem_horizontal_grids#GMAO_0.5_x_0.625_grid

http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-Chem_horizontal_grids#GMAO_0.25_x_0.3125_grid

http://wiki.seas.harvard.edu/geos-chem/index.php/HEMCO_data_directories

http://wiki.seas.harvard.edu/geos-chem/index.php/HEMCO_data_directories

https://www.epa.gov/sites/production/files/2015-10/documents/nei2011v2_tsd_14aug2015.pdf

https://www.canada.ca/en/environment-climate-change/services/pollutants/air-emissions-inventory-overview.html


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used in Canada. Meteorology-driven natural emissions (i.e., volcanic SO2, biogenic VOCs, soil NOx, sea-salt, lightning NOx, mineral dust) are calculated within GEOS-Chem as a function of local values of meteorological variables (temperature, insolation, soil moisture, precipitation, wind speed, convective cloud tops). For volcanic SO2, we recommend only including volcanic degassing in GEOS-Chem simulations unless year-specific volcanic eruption emissions become available.

For the future-years 2020 and 2028, we suggest developing scale factors to project the latest year in each emission inventory to these years.

Table 2-3. GEOS-Chem emission inventories recommendations.*

Region/Emission Type Inventory

Included by Default

in Standard Simulation?

Base Year

2012 Simulation

2016 Simulation

Continental United States NEI monthly1 Yes 2011 2011

Canada APEI2 Yes 2012 2014

Mexico CEDS3 Yes 2012 2014

China MEIC4 No5 2012 2016 Rest of World (including Alaska

and Hawaii) CEDS Yes 2012 2014

Shipping

U.S. Coast - NEI hourly No 2011 2011

Europe Coast – EMEP6 Yes 2012 2012

Rest of World – CEDS Yes 2012 2014

Aircraft AEIC7 Yes 2005 2005

Biomass Burning QFED8 No 2012 2016

C2H6 from oil, gas, and biofuel Tzompa-Sosa et al., 2017 Yes 2010 2010

Volcanic Degassing SO2 AEROCOM9 Yes 2009 2009

Lightning NASA LIS/OTD High Resolution

Monthly Climatology Yes 2012 2016

Natural NH3 GEIA10 Yes 1990 1990

Bromocarbon and Iodocarbon In-line Calculations Yes 2000 2000

Methane Concentrations CMDL flask observations11 Yes 2012 2016 Meteorology-Driven Natural

Emissions In-line Calculations Yes 2012 2016

1Recent discussions with the EPA suggest that use of monthly NEI emissions are more appropriate than hourly emissions when performing GEOS-Chem runs for years other than 2011. 2http://ftp.as.harvard.edu/gcgrid/data/ExtData/HEMCO/APEI/v2016-11/README 3http://wiki.seas.harvard.edu/geos-chem/index.php/CEDS_anthropogenic_emissions 4MEIC emissions as described in Zheng et al. (2018). Expected to be available in GEOS-Chem version 12.3.0. 5The current default inventory in China is the MIX Asian regional emission inventory for 2008 and 2010. 6http://wiki.seas.harvard.edu/geos-chem/index.php/EMEP_European_anthropogenic_emissions 7http://ftp.as.harvard.edu/gcgrid/data/ExtData/HEMCO/AEIC/v2015-01/README 8http://wiki.seas.harvard.edu/geos-chem/index.php/QFED_biomass_burning_emissions 9http://wiki.seas.harvard.edu/geos-chem/index.php/Volcanic_SO2_emissions_from_Aerocom. 10http://ftp.as.harvard.edu/gcgrid/data/ExtData/HEMCO/NH3/v2014-07/README 11 http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-Chem_v11-02#Update_CH4_latitude_bands_for_2014-2016 *Last access to the weblinks above was on February 22, 2019


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There are other emission inventories available in GEOS-Chem that are not presented in Table 2-4. These inventories are either not the most recent emission inventory available or so new to GEOS-Chem that they may have not been fully vetted as noted in Table 2-4. The default global biomass burning inventory is GFED4, which includes 1997-2016 monthly-mean emission files along with daily and hourly emission fractions. We recommend using QFED (version 2.5r1) daily emissions because the inventory includes emissions for year 2016 and no additional scaling is required to convert monthly-mean emissions into daily emissions. Other inventories overwritten by CEDS are neither recommended nor listed in Table 2-3 or Table 2-4.

Table 2-4. GEOS-Chem Emission inventories not recommended.

Region/Emission Type Inventory Base Year Reason for Exclusion

Anthropogenic and Biofuel

Africa DICE 2013 Relatively new

Europe EMEP 2012 CEDS was built off of EMEP and extends to a more recent year

East Asia and parts of Central Asia and Russia

MIX 2010 MEIC extends to 2017 in China,

CEDS extends to 2014 in Central Asia and Russia

Biomass Burning

Global GFED v4.1 2016

QFED emissions have daily resolution while daily and hourly fractions must be

applied to GFED monthly-mean emissions

Natural Emissions

Arctic Ammonia from sea birds 1990 Relatively new; small impacts

Global Aldehyde from decaying

plants 1985 Relatively new

2.2.2 Emission Projections

We recommend applying emission scale factors to each anthropogenic emission inventory described in the previous section for the 2012, 2016, 2020, and 2028 simulations where year-specific emissions are not already available. Natural source emissions depend on meteorology, so no scale factors shall be applied. Below are possible data sources that can be used to develop emission scale factors.

2.2.2.1 United States NEI

The 2012, 2016, 2020, and 2028 CONUS emissions can be calculated by applying scale factors to the 2011 NEI hourly emission inventory included in GEOS-Chem. EPA developed multiple future year emission inventories (i.e., 2017, 2023, 2025, 2028) based on the 2011 NEI inventory and used them for regulatory applications.

2.2.2.2 Canada APEI

The APEI inventory in GEOS-Chem is available through 2014. ECCC published Canada emission trends through 2016 (ECCC, 2018) and provided 2025 projection from 2013, as available in the projected NEI 2028 modeling platform6.

6 https://www.epa.gov/sites/production/files/2017-11/documents/2011v6.3_2023en_update_emismod_tsd_oct2017.pdf

https://www.epa.gov/sites/production/files/2017-11/documents/2011v6.3_2023en_update_emismod_tsd_oct2017.pdf


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2.2.2.3 China-focused literature

Multiple literatures suggest reductions of both NOx and SO2 emissions in China in recent years. Recent satellite data suggest decreasing NOx emissions since 2012 and earlier for SO2 (van der A et al., 2017; Krotkov et al., 2016). The Ministry of Ecology and Environment of China reported decreasing emissions trends and set lower emission targets for 2017 and 2020 (MEE, 2016) despite anticipated increase of the country energy consumption (NBS, 2018).

Wang et al. (2014) provided future-year China emission projections for 2020 and 2030 based on the MEIC 2010 emissions. They developed two energy scenarios: Business-As-Usual (BAU) and Alternative Policy (PC). The BAU scenario is based on current regulations and implementation status (through year 2010) and the PC scenario assumes that energy-saving policies and efficiency improvements will be enacted and enforced. Each energy scenario contains several sub-emission scenarios ([0],[1],[2]) that assume various energy-saving and emission reduction plans. BAU[0] yields the highest emissions and PC[2] yields the lowest emissions. We found PC[1] scenario to be most consistent with the government estimated energy assumption and emission trends mentioned above.

2.2.2.4 Representative Concentration Pathway (RCP) for rest of the world

The RCP Database7 can be used to develop 2020 and 2028 projection factors for the CEDS global emission inventory, the AEIC global aircraft emission inventory, and the CEDS and EMEP shipping emission inventories. It can also be considered for Alaska and Hawaii emission projections.

The RCP data serve as input for climate and atmospheric chemistry modeling as part of the Intergovernmental Panel on Climate Change (IPCC) process and other global modeling studies. There are four RCP scenarios available and all RCPs include the assumption that air pollution control becomes more stringent which causes emissions to decrease over time (Figure 2-1). In general, the lowest emissions are found for the scenario with the most stringent climate policy (RCP2.6) and the highest for the scenario without climate policy (RCP8.5), although this does not apply to all regions, at all times. The RCP4.5 and RCP6 scenarios are intermediate scenarios and have similar NOx and SO2 emission reductions out to 2028.

We recommend that the RCP4.5 emission projection scenario be selected. It is a stabilization scenario where total radiative forcing is stabilized before 2100 by employment of a range of technologies and strategies for reducing greenhouse gas emissions. Because the RCP4.5 data are only available at 5-10 year intervals (e.g., 2010, 2020, 2025, 2030, etc.), emission projections may be linearly interpolated from changes between the two nearest available years.

7 https://tntcat.iiasa.ac.at:8743/RcpDb/dsd?Action=htmlpage&page=welcome

https://tntcat.iiasa.ac.at:8743/RcpDb/dsd?Action=htmlpage&page=welcome


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Figure 2-1. Emissions of SO2 and NOx (top) and greenhouse gas (bottom) across the RCPs. Source: van Vuuren et.al. 2011.

OECD90 = Organisation for Economic Co-operation and Development 90 countries (Western Europe, Canada, US, and Pacific OECD); REF = countries in Reforming Economies region; ASIA = China + India + and Rest of Asia; MAF=Middle East and African countries; LAM = Latin American countries.

Figure 2-2. Five World Regions in the representative concentration pathway.


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MEASUREMENT DATA FOR MODEL PERFORMANCE EVALUATION

3.1 Global Observation Data Sources Global modeling uses relatively coarse grid resolution (e.g., 2x2.5 degree) and covers large areas. While surface monitors may be more populated in urban areas, the model coarse grid resolution may limit the model performance in the urban areas. Rural areas may have sparser observations. Recognizing these limitations, GEOS-Chem MPE could benefit from complimenting surface evaluation with non-surface evaluation against measurements from satellites and sondes. Nonetheless, the non-surface measurements do have limitations. For example, satellites provide total column measurements and rely on vertical retrieval products to interpret vertical distribution information. The retrieval products vary and add uncertainties to the interpreted measurements.

This section lists key features from many available global observation datasets. Surface-level observations are provided, as well as observations that can be used to evaluate vertical distribution of pollutants. Many global and national observational networks exist and only the most relevant networks are included in this report. Our emphasis is on the networks outside of the US, although we provide some information on the US networks for completeness.

Section 2.1.1. provides an overall summary of each global dataset. Section 2.1.2 provides more details about each global dataset.

3.1.1 Overall Summary

Table 3-1 summarizes available global observation datasets and ranks them according to the datasets that are best suited for GEOS-Chem model performance evaluations


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Table 3-1. Available observation datasets.

Rank Network Species Coverage Period Resolution Rural vs Urban

distinction 1 WDCRG1 Gas-phase Surface, many global sites Varies by site, some

through 2019 Typically hourly, sometimes daily

Rural or remote

2 WOUDC Ozone 3-D, global Varies by site, some through 2019

Every few days Mixed; many are coastal

3 EMEP1 Gas + particulate

Surface, Europe-only Varies by site, some through 2019

Typically hourly, sometimes daily

Generally rural; some overlap with WDCRG

4 ACTRiS1 Gas + particulate

Surface, mainly Europe Varies by site, some through 2019


Generally rural; some overlap with WDCRG and EMEP

5 Satellite (TES/OMI)

Gas-phase Surface and 3-D, global TES: 2004-2018 OMI: 2004-present

TES: Every other day OMI: Daily

Difficult to identify rural areas

6 TOAR Ozone Surface, many global sites 1970-2014 Daily through annual Difficult to identify rural areas

7 AirNow DOS

Ozone, total particulate

Surface, ~40 global sites Varies by site, some from 2012-present

Hourly Urban

8 IAGOS Gas-phase 3-D, global aircraft Generally 2011-present

500 flights per year; sub-minute data

Difficult to identify rural areas

9 CASTNET Gas-phase Surface, U.S. only Varies; 2011-2019 for most species

Hourly, daily, weekly Mostly rural

10 IMPROVE Particulate Surface, U.S. only Varies by site Every few days Rural 11 AQS Gas-phase Surface, U.S. only Varies by site Hourly through annual Mostly urban 12 TOLNET Ozone Surface and 3-D, U.S. only 2009-2019 Sporadic, at best 10-12

records a month Urban

13 Field Campaigns

Gas + particulate

Various locations Various time periods Variable Varied

14 AMAP1 Gas + particulate

Surface, Arctic only Varies by site, some through 2019

Typically daily Rural

1The file download process is identical for these networks and the file formats are the same.


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3.1.2 Global Observation Dataset Descriptions

This section describes the global observation datasets listed above in more detail. We only consider data that are in the public domain and strongly advise consulting the data policy for each observational network for guidance on data use acknowledgement.

3.1.2.1 World Data Centre for Reactive Gases (WDCRG) The World Data Centre for Reactive Gases (WDCRG) was established in 2016 and is the data repository and archive for reactive gases of the World Meteorological Organisation’s (WMO) Global Atmosphere Watch (GAW) programme (https://www.gaw-wdcrg.org/; last accessed on 04/02/19).

• Data Access: http://ebas.nilu.no (last accessed on 04/02/19) • Species: SO2, ozone, oxidized nitrogen species, speciated VOCs (units: µg/m3) • Coverage: Roughly 70 sites, station number depends on species • Temporal Resolution: Typically hourly, occasionally daily • Observation Period: Variable by site, some sites have data into 2019 • Data Format: Text files

Map from ebas.nilu.no (last accessed 04/02/19).

Figure 3-1. World Data Centre for Reactive Gases (WDCRG) Sites (top left=O3, top right=NOx, bottom left=SO2, bottom right=VOC).

3.1.2.2 World Ozone and Ultraviolet Radiation Data Centre (WOUDC) The World Ozone and Ultraviolet Radiation Data Centre (WOUDC) was established in 1960 and is part of the World Meteorological Organisation’s (WMO) Global Atmosphere Watch (GAW) programme (https://woudc.org/about/index.php/; last accessed on 04/02/19). The WOUDC is operated by the Meteorological Service of Canada, which is a branch of Environment and Climate Change Canada. The network includes measurements of total column ozone, vertical ozone profiles, ozone lidar measurements, and ozonesonde flights. The National Oceanic and Atmospheric Administration

http://www.wmo.int/pages/prog/arep/gaw/gaw_home_en.html


https://www.gaw-wdcrg.org/

http://ebas.nilu.no/


https://woudc.org/about/index.php

https://woudc.org/about/index.php


11

(NOAA)’s ozonesondes measurements8 can be retrieved from this data center. The WOUDC ozonesonde network is described in more detail below:

• Data Access: https://woudc.org/data/explore.php (last accessed on 04/02/19) • Species: Ozone (unit: partial pressure in millipascals) • Coverage: Global, over 500 registered sites • Temporal Resolution: Every few days • Observation Period: Variable by site, some sites have data into 2019 • Data Format: CSV files

Map from https://woudc.org/data/explore.php (last accessed 04/02/19).

Figure 3-2. World Ozone and Ultraviolet Radiation Data Centre (WOUDC) sites.

3.1.2.3 European Monitoring and Evaluation Programme (EMEP) The European Monitoring and Evaluation Programme (EMEP) is part of the Convention on Long-Range Transboundary Air Pollution (https://www.emep.int/; last accessed on 04/02/19). The EMEP network is considered a rural network representing regional scale atmospheric composition (Henne et al., 2010; Tørseth et al., 2012). Some EMEP sites also report to WDCRG.

• Data Access: http://ebas.nilu.no (last accessed on 04/02/19) • Species: Toxic air pollutants, metals, SO2, ozone, oxidized nitrogen species, speciated VOCs,

other particulates (units: µg/m3) • Coverage: European, number of sites depend on species • Temporal Resolution: Typically hourly, sometimes daily • Observation Period: Variable by site, some sites have data into 2019 • Data Format: Text files

8 https://www.esrl.noaa.gov/gmd/ozwv/ozsondes/

https://woudc.org/data/explore.php

https://www.emep.int/


https://www.esrl.noaa.gov/gmd/ozwv/ozsondes/


12

Map from ebas.nilu.no (last accessed 04/02/19).

Figure 3-3. European Monitoring and Evaluation Programme (EMEP) sites.

3.1.2.4 Aerosol, Clouds, and Trace Gases Research Infrastructure (ACTRiS) The ACTRiS database was initiated in 2011 through an EU initiative funded by Member States and the European Commission (https://www.actris.eu/default.aspx; last accessed on 04/02/19). The ACTRiS program builds upon several prior research collaborations, including European Aerosol Research Lidar Network (EARLINET), European Supersites for Atmospheric Aerosol (EUSSAR), CREATE, and Cloudnet. Some ACTRiS sites also report to WDCRG.

• Data Access: http://ebas.nilu.no (last accessed on 04/02/19) • Species: Toxic air pollutants, metals, SO2, ozone, oxidized nitrogen species, speciated VOCs,

other particulates (units: µg/m3) • Coverage: Mainly European, with several additional sites in the Northern and Southern

Hemisphere. • Temporal Resolution: Typically hourly, sometimes daily • Observation Period: Variable by site, some sites have data into 2019 • Data Format: Text files

https://www.actris.eu/default.aspx



13

Map from ACTRiS website (http://www.actris.net/language/en-GB/Stations.aspx; last accessed 04/03/19).

Figure 3-4. Aerosol, Clouds, and Trace Gases Research Infrastructure (ACTRiS) sites.

3.1.2.5 Tropospheric Emission Spectrometer (TES) The Tropospheric Emission Spectrometer (TES) was aboard NASA’s Earth Observing System (EOS) Aura spacecraft (polar, sun-synchronous) and is a high-resolution imaging infrared Fourier-transform spectrometer. TES provided total column measurements.

• Data Access: https://eosweb.larc.nasa.gov/project/tes/tes_table (last accessed on 04/03/19) • Species: Ozone, H2O, CO, CH4, NO2, HNO3, NH3, CH2O2, CH3OH, COS, PAN (units: variable) • Coverage: Global with swath dimensions 5.3 x 8.5 km; vertical retrieval products available

that include vertical distribution information • Temporal Resolution: 16 orbits every other day; daily and monthly files are available • Observation Period: 2004 – January 31, 2018 • Data Format: NetCDF and HDF-EOS5

Figure 3-5 below shows an example of a Tropospheric Emission Spectrometer (TES) Global Survey, showing estimated ozone values at 681.3 hPa (Worden et al., 2007). This map illustrates the coverage obtained in 16 orbits (~25 hours), in this case, starting on November 10, 2004. The boxes indicate measurement locations but are larger than the actual TES footprint.

https://eosweb.larc.nasa.gov/project/tes/tes_table


14

Figure 3-5. Tropospheric Emission Spectrometer (TES) Global Survey example.

3.1.2.6 Ozone Monitoring Instrument (OMI) The Ozone Monitoring Instrument (OMI) is aboard NASA’s Earth Observing System (EOS) Aura spacecraft (polar, sun-synchronous) and is a nadir-viewing, wide-field imaging spectrometer that uses hyperspectral imaging (https://aura.gsfc.nasa.gov/omi.html; last accessed on 04/03/19). Similar to TES, OMI provides total column measurements.

• Data Access: https://disc.gsfc.nasa.gov/datasets?page=1&source=AURA%20OMI (last accessed on 04/03/19)

• Species: Ozone, NO2, SO2, BrO, C2HO, OClO (units: variable) • Coverage: Global with swath dimensions 13 x 25 km; vertical retrieval products available that

include vertical distribution information • Temporal Resolution: Daily • Observation Period: 2004 – present • Data Format: NetCDF and HDF-EOS5

3.1.2.7 Tropospheric Ozone Assessment Report (TOAR) The TOAR is a database of global surface ozone observations that has been developed and populated with hourly measurement data and enhanced metadata. The database is the largest collection of in-situ hourly surface ozone data in the world. (http://igacproject.org/sites/default/files/2018-04/TOAR_FAQ.pdf; last accessed on 04/03/19).

https://aura.gsfc.nasa.gov/omi.html

https://disc.gsfc.nasa.gov/datasets?page=1&source=AURA%20OMI

http://igacproject.org/sites/default/files/2018-04/TOAR_FAQ.pdf

http://igacproject.org/sites/default/files/2018-04/TOAR_FAQ.pdf


15

• Data Access: Interactive webpage allows for download of O3 averaging periods and statistics by site (daily, monthly, seasonal, annual): https://join.fz-juelich.de. In addition, this site describes how scripts can be written to query the TOAR database: https://join.fz-juelich.de/services/rest/surfacedata/

• Species: Ozone (units: nmol/mol) • Coverage: Global, 10,000 measurement sites including the US CASTNET and AQS networks • Temporal Resolution: Hourly through Annual • Observation Period: 1970–2014; gridded available from 1990-2014 • Data Format: Gridded NetCDF, text file

Map from https://join.fz-juelich.de (last accessed 04/03/19).

Figure 3-6. Tropospheric Ozone Assessment Report (TOAR) sites.

3.1.2.8 EPA AirNow Department of State (AirNow DOS) The EPA AirNow Database is a collection of sites placed at U.S Embassies throughout the world. (https://airnow.gov/index.cfm?action=airnow.global_summary; last accessed on 04/03/19).

• Data Access: Historical, https://airnow.gov/index.cfm?action=airnow.global_summary • Species: Ozone, PM2.5 (units: µg/m3) • Coverage: Global, ~40 U.S. Embassy locations • Temporal Resolution: Hourly, but some sites missing large portions of data • Observation Period: Variable by site, ranging from 2012-2019 • Data Format: CSV files

https://join.fz-juelich.de/

https://join.fz-juelich.de/services/rest/surfacedata/

https://join.fz-juelich.de/services/rest/surfacedata/

https://airnow.gov/index.cfm?action=airnow.global_summary

https://airnow.gov/index.cfm?action=airnow.global_summary


16

Map from https://airnow.gov/index.cfm?action=airnow.global_summary (last accessed 04/03/19). Numbers on map represent Air Quality Index (AQI) at sites on 04/03/19.

Figure 3-7. EPA AirNow Department of State (DOS) sites.

3.1.2.9 In-service Aircraft for Global Observing System (IAGOS) The IAGOS network is a European Research Infrastructure that collects reactive trace gas, greenhouse gas, aerosol number density and size distribution, and cloud particle observations during aircraft flights. The IAGOS-CORE network continuously monitors air pollutants on several aircraft during roughly 500 flights per year. The IAGOS-CARIBIC network contains more in-depth data from one aircraft during 40-50 flights per year. (https://www.iagos.org/; last accessed on 04/03/19).

• Data Access: https://www.iagos.org/iagos-data/ (last accessed on 04/03/19) • Species: CO2, CH4, H2O, N2O, CFCs, SF6, Ozone, CO, NOy, NOx, SO2, VOC, HCHO (units: ppb) • Coverage: Global • Temporal Resolution: Up to 500 flights per year, observations are typically sub-minute

resolution • Observation Period: 2011 through present for most IAGOS CORE datasets; 2018 for CO2 and

CH4 • Data Format: NetCDF or ASCII text files

https://www.iagos.org/

https://www.iagos.org/iagos-data/


17

Map from https://www.iagos.org/ (last accessed 04/03/19).

Figure 3-8. IAGOS network.

3.1.2.10 Tropospheric Ozone LIDAR Network (TOLNET) The Tropospheric Ozone Lidar Network (TOLNET) is a ground-based profiler network for tropospheric ozone. The TOLNET network contains both surface-based and vertical ozone measurements (https://www-air.larc.nasa.gov/missions/TOLNet/; last accessed on 04/03/19).

• Data Access: https://www-air.larc.nasa.gov/missions/TOLNet/data.html (last accessed on 04/03/19)

• Species: Ozone (units: molec/m3 and ppbv) • Coverage: Global • Temporal Resolution: Sporadic; at best there are 10-12 records per month • Observation Period: 2009-2019, but not all years are available at each site • Data Format: ASCII text files

Map from https://www-air.larc.nasa.gov/missions/TOLNet/ (last accessed 04/03/19).

Figure 3-9. Tropospheric Ozone LIDAR Network (TOLNET) sites.

https://www-air.larc.nasa.gov/missions/TOLNet/

https://www-air.larc.nasa.gov/missions/TOLNet/data.html


18

3.1.2.11 Field Campaigns Various locations have local field measurements that vary in spatial and temporal resolution. The data quality and availability of field campaign data is variable. This site contains a list of many field campaigns inside and outside the US: https://www2.acom.ucar.edu/campaigns (last accessed on 04/03/19), including a 2016 joint Korean-NASA field study.

3.1.2.12 Arctic Monitoring and Assessment Programme (AMAP) The Arctic Monitoring and Assessment Programme is a network of observational sites located in the Arctic (https://www.amap.no/ last accessed on 04/03/19).

• Data Access: http://ebas.nilu.no (last accessed on 04/03/19) • Species: Toxic air pollutants, metals, SO2, Ozone, oxidized nitrogen species, speciated VOCs,

other particulates (units: µg/m3) • Coverage: Arctic • Temporal Resolution: Typically daily • Observation Period: Variable by site, some sites have data into 2019 • Data Format: Text files

3.2 Model Performance Evaluation Metrics We recommend evaluating model performance using normalized mean bias (NMB) and error (NME), as well as coefficient of determination (r)9. These statistical measures are commonly used in global and regional modeling applications. Model performance is often compared to a set of numerical “goals” and less restrictive “criteria” performance benchmarks. The purpose of these benchmarks is not to give a passing or failing grade, but rather to put results into the proper context of other model data sets. Emery et al. (2016) recently developed benchmark statistics based on various regional modeling applications (Table 3-2). While we do not expect a global model to have comparable skills to regional models, these benchmarks maybe helpful in putting global model performance in a context of a large array of finer-scale applications.

Table 3-2. Benchmarks for Model Performance Metrics (adapted from Table 4 in Emery et al., 2016).

Species NMB NME r Goal Criteria Goal Criteria Goal Criteria

1-hr or MDA81 O3 <±5% <±15% <15% <25% >0.75 >0.50 24-hr PM2.5, SO4, NH4 <±10% <±30% <35% <20% >0.70 >0.40 24-hr NO3 <±15% <±65% <65% <115% None None 24-hr OC <±15% <±50% <45% <65% None None 24-hr EC <±20% <±40% <50% <75% None None

1 Maximum daily 8-hr average

Ramboll identified 12 peer-reviewed studies that compared GEOS-Chem model output with ozone observations. In most studies, modeled and observed ozone concentrations were compared using the MDA8 statistic. However, a recent study by Travis and Jacob (2019) discourage using the MDA8 metric 9 Normalized Mean Bias (NMB) ranges from -100% to +∞ and is calculated using the following formula, where P=predictions and O=observations: ∑ (𝑃𝑃𝑖𝑖−𝑂𝑂𝑖𝑖)𝑁𝑁𝑖𝑖=1∑ 𝑂𝑂𝑖𝑖𝑁𝑁𝑖𝑖=1

. Normalized Mean Error (NME) ranges from 0% to +∞ and is calculated using the following formula, where P=predictions and O=observations: ∑ |𝑃𝑃𝑖𝑖−𝑂𝑂𝑖𝑖|𝑁𝑁𝑖𝑖=1∑ 𝑂𝑂𝑖𝑖𝑁𝑁𝑖𝑖=1

. Coefficient of determination (r) ranges from 0 to 1 and is calculated using the following formula, where P=predictions and

O=observations:� ∑ (𝑃𝑃𝑖𝑖−𝑃𝑃�)(𝑂𝑂𝑖𝑖−𝑂𝑂�)𝑁𝑁𝑖𝑖=1

�∑ (𝑃𝑃𝑖𝑖−𝑃𝑃�)2 ∑ (𝑂𝑂𝑖𝑖−𝑂𝑂�)2𝑁𝑁𝑖𝑖=1

𝑁𝑁𝑖𝑖=1

�.

https://www2.acom.ucar.edu/campaigns

https://www.amap.no/



19

when comparing ozone from global models with observations. Travis and Jacob note that there is a vertical mismatch between the lowest model layer (65-m mid-point) and CASTNET measurements (10-m), insufficient vertical stratification or ozone loss under rainy conditions, and inadequate representation of the day-night transition to stable conditions. Inaccurate characterization of the day-night transition can lead to errors in the timing of the 8-hour MDA8 window. Travis and Jacob suggest restricting the comparison to afternoon hours and dry days only, which is consistent with approaches used in several other studies (Fiore et al., 2005, Hu et al., 2018, Mao et al., 2013).

3.3 Recommended Model Performance Evaluation Configuration for TCEQ 2012 and 2016 GEOS-Chem Modeling Runs

We recommend, at minimum, focusing MPE at rural or remote sites in the northern hemisphere and near the equator (i.e., north of -23 degrees) and excluding mountain areas (see Appendix A). The global model coarse grid resolution may not capture concentration gradients in urban and mountain areas. Comparison against ozonesondes can inform how well the model captures vertical distributions of pollution. We recommend choosing sondes stations close to CAMx modeling boundaries to evaluate vertical transport coming through the boundaries (see Appendix B). Table 3-3 provides specific recommendations for the TCEQ 2012 and 2016 GEOS-Chem model performance evaluation.

We recommend analyzing model performance over temporal scales of 1 month, not to exceed 3 months (i.e., a single season) for ozone and other gaseous species. Evaluating PM2.5 outside the US may be less relevant because PM2.5 is tied closely to local activities. We do not recommend evaluating GEOS-Chem performance following the native measurement resolution (e.g., 1 hour for ozone) because GEOS-Chem likely cannot capture episodic events and these events are not necessarily a good measure for international transport to the US.


20

Table 3-3. Recommended Datasets and Performance Metrics for TCEQ 2012 and 2016 GEOS-Chem MPE.

Rank Network Species MPE

Recommendation Coverage Resolution Performance Tools

1 WDCRG Ozone,

NO2, SO2 2012, 2016

Surface, many global sites


Scatterplots, performance statistics maps

2 WOUDC Ozone 2012, 2016 3-D, global Every few days Vertical plots, daily or monthly

3 EMEP Ozone 2012, 2016 Surface, Europe-

only Typically hourly, sometimes daily


4 ACTRiS Ozone 2012, 2016 Surface, mainly

Europe Typically hourly, sometimes daily


5 Satellite

(TES/OMI) Ozone 2012, 2016

Surface and 3-D, global

TES: Every other day OMI: Daily

Scatterplots, global gridded plots, Monthly

6 TOAR Ozone 2012 Surface, many

global sites Daily through annual global gridded plots


21

3.4 Tools for Global Model Performance Evaluation 3.4.1 Surface Observation Tool: evalwdcrg

Ramboll developed the evalwdcrg tool to compare surface observations of O3, NO, NO2, and SO2 from the GAW WDCRG global network to GEOS-Chem model output. The tool pairs GEOS-Chem model output and observations in time and saves the paired output to csv files. Quantile-Quantile (QQ) plots, scatter plots, and timeseries can be generated from the csv files. Figure 3-10 shows an example Q-Q plot and Figure 3-11 shows an example timeseries plot. Note that model and observations can be compared with units of µg/m3 or ppb.

Figure 3-10. Example Q-Q plot comparing GEOS-Chem modeled ozone with ozone observations from one WDCRG measurement site.

Figure 3-11. Example timeseries plot comparing GEOS-Chem modeled ozone with ozone observations from one WDCRG measurement site.


22

3.4.2 Vertical Ozonesondes Tool: evalwoudc

The U.S. EPA developed the evalwoudc10 tool to compare vertical ozonesonde measurements from the WOUDC global database with photochemical grid model output. The evalwoudc tool is compatible with GEOS-Chem output in netCDF format. Example output is shown below in Figure 3-12 and Figure 3-13.

Figure 3-12. Example WOUDC output comparing modeled and observed ozone concentrations over time.

Figure 3-13. Example WOUDC output showing a time-averaged comparison of modeled and observed vertical ozone concentrations.

10 https://github.com/barronh/evalwoudc

https://github.com/barronh/evalwoudc


23

GEOS-CHEM 2012 AND 2020 MODELING RUNS

4.1 GEOS-Chem Model Setup Ramboll performed four GEOS-Chem simulations including the 2012 basecase, 2012 zero-out-the-rest-of-world (ZROW), 2020 basecase, and 2020 ZROW. The ZROW runs exclude all anthropogenic emissions outside of the US. All four simulations used the MERRA-2 2012 reanalysis meteorology and model configurations presented in Table 2-1. The selection of emission inventories for the 2012 and 2020 basecase follows our recommendations described in Chapter 2 (see Table 4-1). We applied emission scale factors to each anthropogenic emission inventory where year-specific emissions are not already available. TCEQ intends to use the FINN biomass burning inventory in their CAMx modeling, thus we selected the FINN inventory to promote consistency between global and regional modeling. We turned off all international anthropogenic emission inventories in the ZROW simulations

Table 4-1. 2012 and 2020 Basecase and ZROW GEOS-Chem Emission Inventories

Region/Emission Type Inventory

2012 Simulation 2020 Simulation

Base Year

Used In ZROW run?

Base Year

Used In ZROW run?

Continental United States NEI monthly 2011 Y 2011 Y Canada APEI 2012 N 2014 N Mexico CEDS 2012 N 2014 N China MEIC 2012 N 2017 N

Rest of World (including Alaska and Hawaii)

CEDS 2012 Y1 2014 Y1

Shipping U.S. Coast - NEI monthly 2011 Y 2011 Y

Europe Coast – EMEP 2012 N 2012 N Rest of World – CEDS 2012 Y2 2014 Y2

Aircraft AEIC 2005 Y2 2005 Y2 Biomass Burning FINN3 2012 Y 2012 Y

C2H6 from oil, gas, and biofuel

Tzompa-Sosa et al., 2017 2010 Y2 2010 Y2

Volcanic Degassing SO2 AEROCOM 2009 Y 2009 2009

Lightning NASA LIS/OTD High Resolution Monthly

Climatology 2012 Y 2012 Y

Natural NH3 GEIA 1990 Y 1990 Y

Bromocarbon and Iodocarbon

In-line Calculations and Global Emission Estimates

from Literature

GC default

Y GC

default Y

Methane Concentrations CMDL flask observations 2012 Y 2012 Y Meteorology-Driven Natural Emissions

In-line Calculations 2012 Y 2012 Y

1CEDS only used in Alaska and Hawaii in ZROW runs. 2CEDS ship, AEIC aircraft, and global C2H6 emissions only used in rectangular regions surrounding the CONUS and Alaska in ZROW run. 3http://wiki.seas.harvard.edu/geos-chem/index.php/FINNv1_biomass_burning_emissions. Last access on August 12, 2019.

http://wiki.seas.harvard.edu/geos-chem/index.php/FINNv1_biomass_burning_emissions


24

4.1.1 2012 Scaling Factors

Many of the anthropogenic emission inventories in GEOS-Chem contain emission information for year 2012. Table 4-2 presents scaling factors used to scale NEI emissions from 2011 to 2012 and AEIC emissions from 2005 to 2012. The 2011 to 2012 NEI scaling factor is included by default in GEOS-Chem and the 2005 to 2012 AEIC scaling factor was provided by TCEQ.

Scaling factors are not applied to natural emissions.

Table 4-2. 2012 Anthropogenic Emission Scaling Factors.

Inventory Base Year

CO NOx VOC SO2 NH3 PEC POC PSO4

NEI (CONUS – All Sectors) 2011 0.981 0.939 0.986 0.800 0.999 0.995 0.995 0.800

NEI Shipping 2011 0.981 0.939 0.986 0.800 0.999 0.995 0.995 0.800

AEIC Aircraft 2005 0.715 0.715 0.715 0.715 0.715 0.715 0.715 0.715

4.1.2 2020 Scaling Factors

The scaling factors used for modeling year 2020 are presented in Table 4-3. TCEQ provided the 2020 scaling factors for the US NEI and CEDS inventory including shipping emissions. Ramboll developed projections for other inventories including MEIC (China), APEI (Canada), AEIC (global aircraft), and EMEP shipping (Europe). As described in section 2.2.2.3, we derived the NOx and SO2 scaling factors for MEIC using the PC[1] scenario (Wang et al., 2014). Projection factors for other pollutants in China and all pollutants for other inventories were estimated using the RCP4.5 database as described in section 2.2.2.4.

Table 4-3. 2020 Anthropogenic Emission Scaling Factors.

Inventory Base Year


NEI

Surface 2011 0.772 0.654 0.839 0.729 0.996 1.023 1.023 1.023 EGU 2011 0.772 0.593 0.839 0.344 0.996 1.023 1.023 1.023

Point Non-IPM 2011 0.772 0.654 0.839 0.729 0.996 1.023 1.023 1.023 ONG 2011 1.032 0.935 0.809 1.292 0.995 1.294 1.294 1.294 OTP 2011 0.934 0.778 1.054 0.689 1.047 0.808 0.808 0.808

CEDS

LAM 2014 1.003 0.976 0.891 0.921 1.048 0.995 1.007 0.921

OECD90 2014 0.699 0.753 0.878 0.762 1.029 0.843 0.853 0.762

MAF 2014 1.123 1.082 1.004 1.044 1.074 1.137 1.138 1.044

ASIA 2014 1.106 1.084 1.102 1.025 1.082 1.055 1.027 1.025

REF 2014 0.958 0.855 0.968 0.849 1.030 0.842 0.857 0.849

MEIC 2017 1.051 0.840 1.048 0.857 1.039 1.027 1.013 0.857 APEI 2014 0.699 0.753 0.878 0.762 1.029 0.843 0.853 0.843

AEIC Aircraft 2005 0.774 0.774 0.774 0.774 0.774 0.774 0.774 0.774

Shipping CEDS 2014 1.019 1.019 1.045 0.953 1.000 1.043 1.043 0.953 NEI 2011 0.994 0.682 1.025 0.447 0.995 0.585 0.585 0.585

EMEP 2012 1.026 1.025 -- 0.938 -- -- -- --


25

4.1.3 Emission Summaries

The GEOS-Chem emissions for the US and non-US anthropogenic sources are adjusted to represent 2012 and 2020 using the scaling factors developed for this study. Spatial quality assurance of the emissions projection conducted for the project includes the development of emission summaries before and after applying projection factors. HEMCO emission diagnostic output files are inspected to ensure that the emissions are read in correctly. Table 4-4 and Table 4-5 present annual anthropogenic emissions by world region for 2012 and 2020, respectively, and Table 4-6 shows the change in emissions from 2012 to 2020. GEOS-Chem internally overwrites global inventories with user-selected regional anthropogenic inventories are available. The emission summaries below contain some overlap not accounting for the overwrite. Specifically, the CEDS OECD90 emission totals include emissions from the CONUS and Canada, although CEDS is replaced by NEI and APEI in the modeling simulations. Similarly, the CEDS Asia emission totals include emissions from China which are replaced by MEIC in the modeling. Global CEDS shipping emissions are replaced by the NEI and EMEP shipping emissions near the CONUS and Europe, respectively. Emissions of NOx from NEI and EMEP shipping inventories and CEDS SO4 are included in GEOS-Chem but not reported in the tables below.

Between 2012 and 2020, there are notable decreases in CO, NOx, and SO2 emissions over the CONUS, largely attributed to decreases in surface, point non-IPM, and EGU emission sectors. Similar decreases in CO, NOx, and SO2 emissions are estimated for China, Canada, and other countries in the OECD90. For most pollutants, emissions are expected to decrease between 2012 and 2020 in reforming countries and slightly increase in the Middle East, Africa, and Asia. Global shipping emissions are anticipated to remain roughly constant between 2012 and 2020 and slight increases (8%) are expected for global aircraft emissions.

Table 4-4. Anthropogenic Emissions by World Region in 2012.

Inventory Region Emissions (Megatonne or Megatonne N/year)

CO NOx VOC SO2 NH3 PEC POC PSO4 NEI CONUS 37.3 3.2 8.1 4.3 3.4 0.3 0.5 0.1 APEI Canada 6.1 0.5 --1 1.0 0.5 0.04 0.2 0.01 MEIC China 183.5 9.0 15.0 28.8 10.9 1.8 3.2 0.9

CEDS

LAM 46.0 2.6 7.5 6.2 6.4 0.5 1.3 0.2 OECD90 65.8 6.3 12.6 10.7 8.5 0.4 0.9 0.3

MAF 94.4 3.4 26.0 12.0 8.0 1.6 4.3 0.4 ASIA 305.6 16.1 38.2 49.6 28.0 3.9 9.4 1.5 REF 23.8 2.6 5.2 12.6 2.9 0.4 1.0 0.4

AEIC Aircraft Global 0.5 0.6 0.1 0.2 -- 0.004 0.004 0.005

Shipping Global 0.7 0.4 2.6 9.0 0.02 0.2 0.1 0.3 CONUS 0.01 -- 0.003 0.2 -- 0.00001 0.0003 0.0007 Europe 0.4 -- -- 1.8 -- -- -- --

1CEDS provides Canada VOC emissions.

Table 4-5. Anthropogenic Emissions by World Region in 2020

Inventory Region Emissions (Megatonne or Megatonne N/year)


NEI CONUS 29.3 2.2 6.9 2.5 3.4 0.3 0.5 0.1 APEI Canada 4.2 0.4 --1 0.7 0.5 0.03 0.1 0.01


26

MEIC China 146.2 5.7 16.1 9.2 10.9 1.3 2.2 0.3

CEDS

LAM 44.5 2.5 6.6 6.1 6.8 0.5 1.3 0.2 OECD90 44.5 4.4 10.7 7.7 8.8 0.4 0.8 0.2

MAF 107.0 3.7 26.6 12.6 9.0 1.8 5.0 0.4 ASIA 341.7 17.3 42.2 52.1 30.8 4.2 9.9 1.6 REF 21.6 2.1 4.9 9.0 3.0 0.3 0.8 0.3

AEIC Aircraft Global 0.6 0.6 0.1 0.2 -- 0.004 0.004 0.01

Shipping Global 0.7 0.4 2.6 8.5 0.02 0.2 0.1 0.3 CONUS 0.01 -- 0.003 0.01 -- 0.00001 0.0002 0.0005 Europe 0.4 -- -- 1.7 -- -- -- --

1CEDS provides Canada VOC emissions.

Table 4-6. Change in Anthropogenic Emissions from 2012 to 2020 (% change: [2020-2012]/2012)

Inventory Region Change in Emissions from 2012 to 2020

CO NOx VOC SO2 NH3 PEC POC PSO4 NEI CONUS -21% -31% -15% -43% -0.3% 3% 3% 27% APEI Canada -31% -28% -- -28% 5% -22% -15% -20% MEIC China -20% -36% 7% -68% -0.01% -27% -33% -68%

CEDS

LAM -3% -3% -12% -1% 6% -1% 1% -1% OECD90 -32% -30% -14% -28% 3% -19% -14% -28%

MAF 13% 11% 2% 5% 12% 16% 15% 5% ASIA 12% 8% 11% 5% 10% 7% 5% 5% REF -9% -20% -7% -28% 3% -19% -17% -28%

AEIC Aircraft Global 8% 8% 8% 8% -- 8% 8% 8%

Shipping Global 3% 2% 1% -5% 1% 5% 5% -5% CONUS 1% -- 4% -44% -- -41% -41% -27% Europe 3% -- -- -6% -- -- -- --

4.2 GEOS-Chem Modeling Results GEOS-Chem results were checked for reasonableness through spatial plots of quarterly-average ozone, quarterly-maximum ozone, and quarterly-average PM2.5 in the CONUS.

4.2.1 Spatial Ozone and PM2.5 Plots for the 2012 simulations

Figure 4-1 shows that quarterly-average ozone concentrations range from 13 to 60 ppb in the 2012 basecase in the CONUS, with the highest values located in the Midwest during summer. Excluding anthropogenic emissions outside of the US decreases average ozone concentrations by roughly 2 to 10 ppb across the CONUS, and by over 20 ppb in parts of Mexico and the Pacific Ocean.

The quarterly-maximum ozone concentrations range from 22 to 107 ppb in the basecase (Figure 4-2). In both the basecase and the ZROW simulations, the highest ozone concentrations are found in the eastern half of the US during the summer (Quarter 2 and 3). Quarterly-maximum ozone concentrations are decreased by 0.3 to 37 ppb when international anthropogenic emissions are


27

excluded in the model. Differences in quarterly-maximum ozone values are smaller in the eastern US compared to the quarterly-average ozone plots shown in Figure 4-1.

Figure 4-3 shows that quarterly-average PM2.5 concentrations range from 0.8 to 23.8 µg/m3 in the 2012 basecase simulation and are decreased by roughly 0.1-5 µg/m3 in the ZROW run. The largest decreases in PM2.5 are found in Canada and Mexico, which are locations where all anthropogenic emissions have been turned off in the ZROW run.

4.2.2 Spatial Ozone and PM2.5 Plots for the 2020 simulations

Figures 4-4 through Figure 4-6 indicate that the spatial patterns in ozone and PM2.5 from the 2020 GEOS-Chem simulations are generally similar to the 2012 modeling results (Figure 4-1 through Figure 4-3). In addition, the changes in ozone and PM2.5 when international emissions are excluded in GEOS-Chem are similar in 2012 and 2020. Across the modeling domain, ozone and PM2.5 concentrations are lower in 2020 compared to 2012, which is consistent with projected emission decreases in the US, Canada, and China between 2012 and 2020.


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Figure 4-1. Quarterly-average surface ozone mixing ratios for 2012 basecase simulation (left), 2012 ZROW simulation (middle), and the difference between the two simulations (right).


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Figure 4-2. Quarterly-maximum surface ozone mixing ratios for 2012 basecase simulation (left), 2012 ZROW simulation (middle), and the difference between the two simulations (right).


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Figure 4-3. Quarterly-average surface PM2.5 mixing ratios for 2012 basecase simulation (left), 2012 ZROW simulation (middle), and the difference between the two simulations (right).


31

Figure 4-4. Quarterly-average surface ozone mixing ratios for 2020 basecase simulation (left), 2020 ZROW simulation (middle), and the difference between the two simulations (right).


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Figure 4-5. Quarterly-maximum surface ozone mixing ratios for 2020 basecase simulation (left), 2020 ZROW simulation (middle), and the difference between the two simulations (right).


33

Figure 4-6. Quarterly-average surface PM2.5 mixing ratios for 2020 basecase simulation (left), 2020 ZROW simulation (middle), and the difference between the two simulations (right).


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CONCLUSIONS The objectives and goals of this study are designed to help TCEQ complete upcoming modeling activities that may utilize GEOS-Chem modeling results from multiple emission scenarios. This report documents four major objectives of this project including:

Objective 1. Recommend GEOS-Chem configurations for these modeling runs, with an emphasis on appropriate emission inventory selection for each modeling year

Objective 2. Compile global observation datasets to facilitate model performance evaluation

Global observation datasets were compiled during this study to enhance GEOS-Chem global model performance evaluations. In addition, tools were tested and developed to facilitate direct comparison of GEOS-Chem model results with gas-phase surface observations from the GAW WDCRG database and ozonesondes from the WOUDC network.

Objective 3. Develop a tool to prepare CAMx boundary conditions.

Ramboll developed the geos2aqm tool to extract and convert GEOS-Chem netCDF output into boundary conditions for regional photochemical grid models (CAMx and CMAQ). Boundary conditions can be generated for both CAMx version 6.5 and earlier (UAM format) and CAMx version 7 and later (netCDF format). The geos2aqm tool is python-based and can either be run using local python libraries or using a pre-compiled, stand-alone executable. Additional details about the geos2aqm tool can be found in the accompanying geos2aqm User Guide.

Objective 4. Conduct four GEOS-Chem scenarios.

Ramboll performed four GEOS-Chem modeling runs; 2012 Basecase, 2012 Zero-Out-Rest-of-World (ZROW), 2020 Basecase, and 2020 ZROW. GEOS-Chem results were checked for reasonableness through spatial plots of ozone and PM2.5. We recommend further evaluating model performance of the 2012 basecase against observations within the US and outside the US as described in Section 3.

Overall, projected 2020 emissions in the US, Canada, and China are considerably lower (~20-68%) than 2012 emissions in the GEOS-Chem, which leads to decreases in modeled ozone and PM2.5 concentrations across the CONUS in the 2020 modeling run. When international anthropogenic emissions are excluded in GEOS-Chem, ozone concentrations are decreased by roughly 2-10 ppb and PM2.5 concentrations are decreased by 0.1-5 µg/m3 over the CONUS in both 2012 and 2020.


35

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APPENDIX A WDCRG Site Recommendation


A-1

Appendix A. WDCRG Site Recommendation

Station Station name Lat Lon Elevation (m)

Ozone NO2 NOx SO2 2012 2016 2012 2016 2012 2016 2012 2016

BB0001R Ragged Point 13.1700 -59.4300 45 x x

BM0001R Tudor Hill (Bermuda) 32.2700 -64.8800 30 x x

CH0002R Payerne 46.8129 6.9435 490 x x x x x x CV0001G Cape Verde Atmospheric Observatory 16.8640 -24.8675 10 x x

CZ0003R Kosetice (NOAK) 49.5833 15.0833 543 x x CZ0005R Churanov 49.0685 13.6197 NA x

EG0001U Cairo 30.0833 31.2833 35 x

EG0002U Hurghada 27.4167 33.7500 7 x

EG0004R Farafra 27.0581 27.9902 92 x

HU0002R K-puszta 46.9667 19.5833 125 x x

IT0014R Capo Granitola 37.6667 12.6500 5 x x IT0015U Lecce (ECO) 40.3358 18.1245 36 x x x

IT0016R Lamezia Terme 38.8763 16.2322 6 x x x

IT0018R Lampedusa 35.5182 12.6305 45 x

JP0003U Tateno 36.0581 140.1258 25 x x

JP1020R Ryori 39.0319 141.8222 260 x x

JP1028G Minamitorishima 24.2883 153.9833 7 x x

JP1029R Yonaguni 24.4667 123.0106 30 x x

LV0010R Rucava 56.1620 21.1732 18 x

LV0016R Zoseni 57.1351 25.9056 183 x

MT0001R Giordan Lighthouse 36.0722 14.2184 167 x x x x NL0009R Kollumerwaard 53.3333 6.2667 0 x x x NO0042G Zeppelin mountain (Ny-Ålesund) 78.9067 11.8893 475 x x x x PL0002R Jarczew 51.8167 21.9833 180 x x x

PL0004R Leba 54.7500 17.5333 2 x x x

SI0008R Iskrba 45.5612 14.8580 540 x x

SI0031R Zarodnje 46.4286 15.0033 770 x

SI0033R Kovk 46.0700 15.0600 600 x

US6005G Trinidad Head 41.0541 -124.1510 107 x x

VN0001R Pha Din 21.5731 103.5157 1466 x


APPENDIX B WOUDC Ozone Sondes Station Recommendation


B-1

Appendix B. WOUDC Ozone Sondes Station Recommendation

Station Name Country Lat Lon Elevation (m) 2012 2016

RPB Ragged Point Barbados 13.1700 -59.4300 45

WSA Sable Island Canada 43.9326 -60.0086 2

TKB Tateno (Tsukuba) Japan 36.0581 140.1258 25.2 x x

KAG Kagoshima Japan 31.5500 130.5500 31

CWB Taipei Taiwan 25.0200 121.4800 5 x

CHE Cheju Korea 33.5000 126.5000 300

AAR Hanoi Vietnam 21.2000 105.8000 25 x

PJM Petaling Jaya Malaysia 3.1000 101.6500 46 x

SIN Singapore Singapore 1.3679 103.9824 14 x

WAT Watukosek (Java) Indonesia -7.5700 112.6500 50 x

SUV Suva Fiji -18.1300 178.3200 6 x x

SCR San Cristobal Ecuador -0.9041 -89.6143 6 x

HEC Heredia Costa Rica 10.0000 -84.0700 1144

ARK Albrook Panama 8.9800 -79.5500 66

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