1. Evaluating Local-scale CO 2 Meteorological Model Transport Uncertainty for the INFLUX Urban Campaign through the Use of Realistic Large Eddy Simulation Brian Gaudet, Thomas Lauvaux, Aijun Deng, Kenneth Davis, and Daniel Sarmiento The Pennsylvania State University Greenhouse Gas Emissions: Quantifying Uncertainties in Measurements and Models and Resultant Climate Impacts III 96 th American Meteorology Society Meeting, New Orleans, LA, 11 January 2016

2. INFLUX Sensors in Indianapolis Background A goal of the Indianapolis Flux (INFLUX) field campaign -- develop an urban inversion system (network of observations  emission field) High-resolution (approximately 1-3 km) mesoscale simulations with the Weather Research and Forecasting – Chemistry (WRF-Chem) model are a key component in the inversion system (model fields needed to predict advective and turbulent transport). Questions: What are the typical transport errors and biases of the WRF output? How do these errors vary over spatial / temporal scales? (in particular the near field) 2

3. Potential Sources of Transport Error in Near-Source Region Measured concentrations may be unrepresentative of model grid-cell average (representativeness error). Model resolution may be insufficient when on order of source / receptor distance Model representation of turbulence may be insufficient when turbulent eddy size is on order of source / receptor distance Model does not predict turbulent fields, just (Reynolds) averaged turbulent fluxes / variances (statistics) through the PBL scheme Only vertical turbulent fluxes are predicted (usually Δx >> eddy length scale) Turbulent tracer transport assumed to be downgradient with K a function of turbulent statistics (possibly with countergradient term) 3

4. Design: Compare high resolution mesoscale simulation to equal or higher resolution large eddy simulation (WRF-Chem-LES) configuration, with largest PBL turbulent eddies appearing explicitly in the simulation (and thus, turbulent transport of tracers need not be parameterized, but is explicitly resolved). Case study from INFLUX with relevant quiescent meteorology and southerly flow selected for modeling of (28 Sep 2013). One-way nested grid simulation performed from 1200 – 0000 UTC (0700 – 1900 LST) based on 32-km North American Regional Reanalysis (NARR) 4

5. WRF-Chem (Grell et al. 2005) Baseline Nested Model Configuration: Tracer represents emissions from Harding St. power plant (39.71N, 86.20W) as provided by Hestia database (Gurney et al. 2012) Grids 1-4 use Mellor-Yamada-Nakanishi-Niino (MYNN) PBL parameterization for turbulent transport (Δx = 9 km, 3 km, 1 km, 333 m) Grid 5 run as LES (Deardorff 1980) (Δx = 111 m) Case: 28 September 2013 (150 x 150) 10 km 5

6. Instantaneous Plumes (Top View): Baseline Grid-4 (333-m mesoscale PBL) vs. Grid 5 (111-m LES)

7. Both mesoscale configuration and LES capture wind direction shift, consistent with observed meteorology Convective PBL scaling parameters are similar in each simulation (z i = 1500 m; w * =2 m s -1 ; U = 7 m s -1 ) In convective PBL, LES plume is much more irregular, and shows higher maximum concentrations 10 km z = 84 m ppmv 409.6 204.8 102.4 51.2 25.6 12.8 6.4 3.2 1.6 0.8 0.4 0.2 333-m mesoscale PBL111-m LES 1600 LST 28 Sep 2013

8. Instantaneous Cross-sections of Integrated Plume Concentrations (integrated in cross-plume direction) Including Baseline Configuration and Cross- Configurations (111-m mesoscale PBL; 333-m LES)

9. In far-source region (>> U z i / w * ), maximum plume vertical extent in afternoon is generally (but not always) similar between the LES and mesoscale configuration In near-source region, however, LES plume much shallower, and displays correspondingly higher maximum concentrations 9 ppmv 409.6 204.8 102.4 51.2 25.6 12.8 6.4 3.2 1.6 0.8 0.4 0.2 (~ U z i / w * ) 1600 LST 28 Sep 2013 1300 LST 28 Sep 2013

10. LES vs. mesoscale physics seems responsible for the systematic near-source differences 10 ppmv 409.6 204.8 102.4 51.2 25.6 12.8 6.4 3.2 1.6 0.8 0.4 0.2 1300 LST 28 Sep 2013 1600 LST 28 Sep 2013

11. Time-Integrated Plume Concentrations (Top View) (time-integrated every 10 min from 1500 LST until 1700 LST)

12. LES plume is wider than mesoscale PBL plume after averaging (Recall that horizontal diffusion in mesoscale PBL is not a function of predicted turbulence, but numerical – with grid spacing as length scale, not turbulence scale) 10 km z = 84 m ppmv 409.6 204.8 102.4 51.2 25.6 12.8 6.4 3.2 1.6 0.8 0.4 0.2 111-m mesoscale PBL111-m LES HSPP Site 3 Site 10 HSPP

13. LES plume does not appear close to the source at higher levels. 10 km z = 199 m ppmv 409.6 204.8 102.4 51.2 25.6 12.8 6.4 3.2 1.6 0.8 0.4 0.2 111-m mesoscale PBL111-m LES HSPP Site 3 Site 10 HSPP

14. Taylor (1921) Lagrangian-based theory of short time / long time diffusion (but applied to vertical) 14 Short Lagrangian Release Time (or Distance) Regime Long Lagrangian Release Time (or Distance) Regime ‘random walk’ velocity increments velocities still correlated with values at release

15. Time-Integrated Cross-sections of Integrated Plume Concentrations (time-integrated every 10 min from 1500 LST until 1700 LST)

16. No temporal average Temporal average Plume structure appears more similar after temporal average is performed, especially in the far-source region. However, differences remain in the near-source region (< 3 km) (LES plume shallower, higher concentrations) Parabolic vs. linear near-source ascent consistent with short-time limitations of mesoscale PBL 111-m mesoscale PBL111-m LES Crosswise-averaged Temporally averaged (every 10 min from 1500 – 1700 LST) 16 ppmv 409.6 204.8 102.4 51.2 25.6 12.8 6.4 3.2 1.6 0.8 0.4 0.2 1 km

17. Willis and Deardorff (1976) (laboratory tank model of particle tracers) 17 Vertical axes: normalized by z i Horizontal axes: normalized by Uz i / w * Linear growth of standard deviation until about X = 0.3 for near-surface release Little ascent of plume maximum until xw * / Uz i > 0.5

18. Summary and Conclusions LES was used as a tool to evaluate the potential skill of a high- resolution mesoscale model (WRF-Chem) in predicting turbulent and advective transport of CO 2 for an urban environment (INFLUX) case study. A 333-m version of the mesoscale model showed good agreement with 111-m LES in terms of transport direction, general turbulence parameters, and vertical plume extent of the far-source region (> U z i / w * ), at least if sufficient temporal averaging of the plume (several PBL eddy turnover times) is performed. 18

19. Summary and Conclusions Temporal averaging of mesoscale plume even at high horizontal resolution cannot capture full spatial extent of the temporally averaged LES plume, because of the absence of explicit turbulent eddies. The mesoscale model also appears to be biased in terms of overpredicting the vertical extent of the temporally averaged plume and underpredicting maximum surface concentrations in the near-source region (roughly < 0.3 U z i / w * ), related to the inherent limitations of the mesoscale PBL scheme in the short-time regime. These results have implications on the use of mesoscale models to infer emissions when large sources are in close proximity to the sensor. 19

20. Acknowledgements The INFLUX project is funded by the National Institute of Standards and Technology. Kevin Gurney (Arizona St.) provide the Hestia emissions data used in this study. Natasha Miles provided the cover photograph. 20