WRF/CMAQ AQMEII3 Simulations of U.S. Regional- Scale Ozone: Sensitivity to Processes and Inputs

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1 Although this work has been reviewed and approved for presentation, it does not necessarily reflect the official views and policies of the U.S. EPA. WRF/CMAQ AQMEII3 Simulations of U.S. Regional- Scale Ozone: Sensitivity to Processes and Inputs C. Hogrefe, P. Liu, G. Pouliot, R. Mathur, and S. Roselle Computational Exposure Division, U.S. Environmental Protection Agency, RTP, NC, USA HTAP/AQMEII/MICS Workshop April 3-4, 2017 RTP, NC Acknowledgments: We gratefully acknowledge Johannes Flemming for providing the C-IFS fields, Rokjin Park for providing the GEOS-CHEM fields, and Meiyun Lin for providing the AM3 fields from their simulations performed for TF-HTAP.

2 Annual 2010 AQMEII3 WRF/CMAQ Simulations AQMEII3 Base Case Simulated by All Modeling Groups Sensitivity Simulations Leveraging HTAP2 Global Simulations Boundary Conditions C-IFS H-CMAQ GEOS-CHEM AM3 Zero Boundary Conditions BC Zero Photochemical Modeling (12km) CMAQ Offline Including Process Analysis CMAQ Offline No O 3 Dry Dep. Meteorological Modeling (12km) WRF 3.4 Emissions AQMEII2 (Pouliot et al., 2015) Zero Anthropogenic Emissions EM Zero Bounding Simulations

3 Model Performance Summary for BASE Simulations Surface Daily Maximum 8-hour Average (MDA8) O 3 Performance Metrics Normalized Mean Bias (NMB), % Normalized Mean Error (NME), % Correlation Coefficient (R) Northwest Intermountain West Midwest Southeast Northeast Spring Summer Fall Winter Spring Summer Fall Winter Spring Summer Fall Winter Color shading based on Emery et al. (2017, in press) recommendations: Meets performance goal and criterion Meets performance criterion but not goal Meets neither performance goal nor criterion Goals: NMB < ± 5%, NME < 15%, R > 0.75 Criteria: NMB < ± 15%, NME < 25%, R > 0. 5 BASE model performance tends to be worse in the NW compared to other regions and in winter compared to other seasons Except for the NW, NMB is negative during winter in all regions, suggesting that large-scale ozone background concentrations specified through model LBC may be underestimated in the BASE simulation

4 Model-Observation MDA8 O 3 Differences Across Observed Percentiles Unpaired-in-time differences tend to be relatively flat across the range of the observed percentiles while the curves for the paired-in-time differences tend to have a negative slope better performance at matching the width of the MDA8 O 3 distribution than capturing the timing of specific observed ozone events MW/SE/NE: Tendency to underestimate in winter, overestimate in summer across most percentiles IMW: Smallest seasonal variation in model performance

5 BASE Process Analysis (PA) Results Vertical Profiles Seasonal Change in O 3 Mass Due to 7 Processes (Domain Total) HADV and ZADV are of similar magnitude and opposite direction and are the dominant processes above ~800 mb Layer 1: DDEP is a strong sink of ozone, balanced largely by VDIF CHEM: sink in layer 1, source in the PBL, and sink between ~ mb for all seasons except winter Vertical mixing due to cloud processes tends to be a source of ozone in the lower atmosphere and sink in the upper atmosphere Horizontal advection (HADV), vertical advection (ZADV), horizontal diffusion (HDIF), vertical diffusion (VDIF), dry deposition (DDEP), chemistry (CHEM), and cloud processes including vertical mixing by convection clouds (CLDS)

6 Process Effects on O 3 Column Mass Monthly Change in O 3 Mass Due to 7 Processes (Domain Total) TADV = HADV + ZADV; TDIF = HDIF + VDIF Changes in ozone mass in the upper layers (above 750 mb) are dominated by TADV The column between 250 mb and 750 mb gains ozone mass through TADV for almost all months, indicating that both lateral boundary conditions and ozone in the upper layers determine the ozone column burden simulated in the free troposphere The ozone column below 750 mb gains mass through the effects of CHEM especially during summer as well as through the effects of vertical mixing by convective clouds that tap into the ozone reservoir in the free troposphere. The dominant sink term of ozone mass in this layer range is DDEP at the surface. PA results indicate that boundary conditions (advection), emissions (chemistry), and dry deposition are key factors in modulating the simulated ozone burden

7 O 3 from Bounding Simulations: Zero BC, Zero EM, No O 3 DDEP O 3 Column Mass (top 3 panels, domain total) and O 3 Surface Mixing Ratio (bottom panel, domain average) Results confirm that the upper layers are completely driven by advection of LBC (BC zero leads to an ozone column of essentially zero, while the results for all other runs are close together) In the FT, there is some impact during summer of zeroing out emissions, but this column range is still dominated by BC effects Both deposition and emissions affect the O 3 mass in lowest column, but LBC effects still dominate At the surface, deposition and LBC are the largest factors.

8 Bounding Simulations Impact on Zero BC, Zero EM and No O 3 DDEP on Surface Mixing Ratios Winter Fall Summer Spring Changes in Seasonal Mean O 3 Surface Mixing Ratios Between Bounding Simulations and BASE BC ZERO - BASE EM ZERO - BASE NO O3 DDEP - BASE Results consistent with domain-average surface O 3 time series, but spatial and seasonal patterns exist Results of brute-force bounding simulations demonstrate that the characterization of ozone outside the regional-scale modeling domain has a profound impact on simulated regionalscale ozone. Utilize HTAP global model fields as well as H-CMAQ to perform CONUS WRF/CMAQ simulations with different BC. This is aimed at investigating the impact of different state-of-science representations of the global atmosphere on air quality simulated over the U.S. with a 12 km resolution regional-scale model.

9 Global/Hemispheric Models Providing Boundary Conditions C-IFS ( Composition Integrated Forecasting System, European Centre for Medium Range Weather Forecasts) CB05/TM5 gas phase chemistry for troposphere; MACC aerosol scheme; stratospheric ozone relaxed towards MACC reanalysis H-CMAQ (U.S. EPA, Computational Exposure Division) WRF3.7/CMAQv5.1 with CB05-E51 gas phase chemistry (incl. full halogen chemistry), AERO6 aerosol scheme; stratospheric ozone scaled to potential vorticity GEOS-CHEM (Seoul National University) GEOS-Chem v , full tropospheric chemistry, climatological representation of stratospheric sources/sinks AM3 (NOAA GFDL) Coupled stratosphere-troposphere chemistry, pressure-dependent nudging to reanalysis winds Vertical Model Levels All models used the HTAP2 anthropogenic emission inventory (Maenhout et al., 2015)

10 Monthly Time-Height Cross Sections of CMAQ O 3 Boundary Conditions Derived from 4 Models for 4 Edges C-IFS H-CMAQ GEOS-CHEM AM3 West South East North Substantial model-tomodel differences in ozone along the four edges of the WRF/CMAQ 12 km modeling domain, both in terms of magnitude and seasonal variations

11 Daily Average Ozone Time Series at ~500 mb Averaged Along the Four Edges of the CMAQ Domain Daily Average Large model-to-model variations in midtropospheric ozone, particularly during spring In addition to seasonal differences, there are also episodic differences Seasonal fluctuations differ between models, with H-CMAQ showing the smallest seasonal variability The nature of model-to-model differences varies between the different edges of the CMAQ domain The differences point to differences in the representation of stratospheric ozone and stratosphere/troposphere exchange

12 CMAQ Daily Average O 3 Column Mass (Domain-Wide) Differences in ozone boundary conditions result in differences of CMAQ-simulated ozone column mass over the modeling domain Differences are most pronounced in spring Using GEOS-CHEM boundary conditions yields the highest burden in the stratosphere Using AM3 boundary conditions yields the highest CMAQ-simulated ozone burden in the PBL and free troposphere

13 O 3 BC C-IFS ( Base ) O 3 BC AM3 - Base Impact of Different Boundary Conditions on CMAQ Simulated Spring Average Surface O 3 Mixing Ratios O 3 BC H-CMAQ - Base O 3 BC GEOS-CHEM - Base Consistent with the analysis of the global models and CMAQ ozone column burdens, the CMAQ BC AM3 simulations exhibit the highest spring surface ozone mixing ratios over the entire modeling domain Differences between the BC H-CMAQ and BC GEOS-CHEM runs w.r.t. base are smaller Separate analysis shows that BC impacts are very similar for hourly and MDA8 O 3

14 O 3 BC C-IFS ( Base ) Impact of Different Boundary Conditions on CMAQ Simulated Summer Average Surface O 3 Mixing Ratios O 3 BC H-CMAQ - Base O 3 BC AM3 - Base O 3 BC GEOS-CHEM - Base Throughout most of the domain, all three sensitivity simulations tend to have higher summertime ozone than the C-IFS driven base case For a large portion of the domain, the BC AM3 run still has the highest concentrations, but the differences are smaller than during spring

15 Comparison of Observed and Simulated MDA8 O 3, AQS Sites Daily Observations and Model Simulations BC C-IFS BC H-CMAQ Daily Bias BC AM3 BC GEOSCHEM Observations BC C-IFS BC H-CMAQ BC AM3 BC GEOSCHEM Monthly Mean Bias BC C-IFS BC H-CMAQ BC AM3 BC GEOSCHEM The choice of boundary conditions can have a significant impact on model bias for MDA8 O 3, especially for the non-summer months

16 Daily Maximum 8-hr O 3 NMB (%) Surface Daily Maximum 8-hour Average (MDA8) Normalized Mean Bias Spring Summer Fall Winter Northwest Intermountain West Midwest Southeast Northeast BC C-IFS BC H-CMAQ BC GEOS-CHEM BC AM BC C-IFS BC H-CMAQ BC GEOS-CHEM BC AM BC C-IFS BC H-CMAQ BC GEOS-CHEM BC AM BC C-IFS BC H-CMAQ BC GEOS-CHEM BC AM Boundary conditions can affect model performance as measured by goals / criteria (e.g. spring in IMW, MW) While wintertime O 3 is underestimated by the BC C-IFS (BASE) runs as shown earlier, the opposite is true for the BC H-CMAQ and BC AM3 simulations

17 Model-Observation MDA8 O 3 Differences Across Observed Percentiles, Paired in Time Boundary conditions can affect model performance across the entire range of the observed distribution, though the impacts tend to be lower during summer and for the very highest observed percentiles

18 Summary Process analysis and bounding simulations highlight the profound impact of boundary conditions on regional-scale ozone simulations throughout the year, both in terms of column loadings and surface concentrations Boundary conditions derived from four different global/hemispheric models were found to cause noticeable differences in WRF/CMAQ model performance There is a growing need for more thoroughly evaluating hemispheric / global scale models used in regional-scale applications and to work towards a consistent treatment of transport and chemistry across scales The coordinated HTAP2 / AQMEII3 simulations provide a rich dataset to help better understand the role of hemispheric background concentrations on regional-scale air quality