in a Walnut Orchard During CHATS

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National Center for Atmospheric Research, Boulder, CO, USA > CHATS Background > Experimental

1 Aerosol Fluxes in a Walnut Orchard During CHATS Andreas Held 12 1,2, Alex Guenther 2, Ned Patton 2, Jim Smith 2, Andrew Turnipseed 2 1 Universität Bayreuth, Bayreuth, Germany 2 National Center for Atmospheric Research, Boulder, CO, USA > CHATS Background > Experimental Setup > Aerosol Flux Observations > Relaxed Eddy Accumulation Simulations > Scalar and Spectral Similarity

2 CHATS Canopy Horizontal Array Turbulence Study

3 CHATS Aerosol Eddy Covariance System Instruments: Campbell Scientific CSAT3 sonic anemometer KH20 hygrometer TSI CPC 3772 condensation particle counter 14 m above ground g 5 m above canopy

4 Instrumental Response Concentration Step Change n ormalized concentrat tion [-] field configuration e -1 change 95 % change Response time: CPC t 95% = 1.26 s CPC + sampling line (field cfg) t 95% = 1.83 s time [s] 1 Correction of fluxes according to Horst (1997) normalized concentr ration [-] field configuration 95 % change e -1 change time [s]

5 Instrumental Response Variation of Sampling Frequency Aerosol Number Flux Buoyancy Flux a) b) ].5 Hz [10 6 m -2 s - aeros sol 2/1/ ] buoy yancy 2/1 1/0.5 Hz [K m s y = 0.995x (2 Hz) y = 0971x x (1 Hz) y = 0.889x (0.5. Hz) y = 0.919x (2 Hz) y = 0.835x (1 Hz) y = 0.691x (0.5. Hz) aerosol 10 Hz [10 6 m -2 s -1 ] buoyancy 10 Hz [K m s -1 ] No significant difference of fluxes for 1, 2, 10 Hz sampling Systematic reduction of flux estimates at lower sampling frequencies Reduced fluxes in case of 0.5 Hz sampling High frequencies not resolved from Held et al. (2008)

6 CHATS Aerosol Number Flux Observations II lux aero osol number f [10 6 m -2 s -1 ] s -1 ] v d [mm aerosol numb ber [cm -3 ] buoya ancy flux [K m s -1 ] u * [m s -1 ] :00 06:00 12:00 18:00 00:00 06:00 12:00 18:00 00: /05/ /05/22 Negligible fluxes during the night Brief emission periods in the early morning Aerosol deposition later in the morning, peaking around noon Number concentration starts to increase during deposition periods 1. Emission of few large particles 2. Deposition of many small particles from Held et al. (2008)

7 Aerosol Emissions Mineral Dust

8 CHATS LIDAR Observations REAL system 22 May, UTC PDT 22 May, UTC PDT 22 May, UTC PDT 22 May, UTC PDT 60 v d [mm s -1 ] :00 06:00 12:00 18:00 00:00 06:00 12:00 18:00 00: /05/ /05/22 Combination of particle emission and deposition! courtesy S. Mayor (CSU Chico)

9 Relaxed Eddy Accumulation (REA) Simulations 1. Estimate eddy covariance flux: F EC w'c' 2. Calculate standard deviation of vertical wind speed: F REA b w( cup cdown) 3. Simulate conditional sampling: c up and c down (updraft and downdraft concentrations) 4. Use different definitions of wind deadband updraft: H REA w' w downdraft: H REA w' w 5. Equate eddy covariance and relaxed eddy accumulation fluxes: F EC =F REA w'c' b ( c c ) down w up

10 Variability of b Factors CPC vs Temperature No deadband: large deviations H REA = 0.6: reduced scatter from mean b CPC [-] b log( z / L) Ammann and Meixner (2002) b T [-] stability dependence :00 12:00 00:00 12:00 00;00 00:00 12:00 00:00 12:00 00; /05/ /05/ /05/ /05/22 from Held et al. (2008) 0.25

11 Variability of b Factors CPC vs Water Vapor No deadband: large deviations H REA = 0.6: reduced scatter from mean b CPC [-] b log( z / L) Ammann and Meixner (2002) b H2O [-] stability dependence :00 12:00 00:00 12:00 00; /05/ /05/ :00 12:00 00:00 12:00 00; /05/ /05/22 from Held et al. (2008)

12 Dependence of b Factor on Wind Deadband Reduction of b: increased concentration difference, stronger updrafts and downdrafts Functional relationships REA observed and proposed: 1 c 1 exp( a H REA) b b least reduction in aerosol case Businger and Oncley (1990) Ammann and Meixner (2002) c = 0.378, a = for aerosol Businger and Oncley (1990) Pattey et al. (1993) Ammann and Meixner (2002) b REA / b b REA / b b CPC [this study] b T [this study] b H2O [this study] b neutral [AM02] b Gauss [AM02] b [BO90] 0.4 b CPC [this study] b T [this study] b H2O [this study] b CO2 [P93] b T [P93] b H2O [P93] b neutral [AM02] b [AM02] Gauss b [BO90] wind deadband H REA wind deadband H REA from Held et al. (2008)

13 Deviation of b Factor Aerosol vs T, H 2 O and constant b General observations: H REA : 0 0.5: Median deviation ~ 10 % H REA : Median deviation < 10 % Minimum of interquartile range Minimum of 95 % percentile H REA > 0.8: Deviations increase Wind deadband recommended! No advantage of proxy scalars over constant b factor! from Held et al. (2008)

14 Scalar and Spectral Correlation Aerosol vs. Temperature Aerosol vs. Water Vapor 1.0 a) CPC vs. T 1.0 b) CPC vs. H 2 O scalar corre elation r [-] scalar corre elation r [-] sp pectral correlatio on r S [-] c) CPC vs. T sp pectral correlatio on r S [-] d) CPC vs. H 2 O high mid low :00 06:00 12:00 18:00 00:00 06:00 12:00 18:00 00:00 00:00 06:00 12:00 18:00 00:00 06:00 12:00 18:00 00: /05/ /05/ /05/ /05/22 from Held et al. (2008)

15 Summary Observed diurnal pattern of aerosol number fluxes by eddy covariance: Brief emission period in the early morning, strong deposition flux around noon REA simulations as a post-processing processing tool to evaluate - high-frequency data quality - flux estimate uncertainty Use of proxy scalars (T, H 2 O) not favorable over constant b factor Wind deadband of 0.6 to 0.8 yields robust results Deadband-induced reduction of b can be parameterized Low scalar similarity between aerosol number concentration and proxy scalars

16 Acknowledgements NCAR/EOL staff German Research Foundation DFG (HE-5214/1-1) 1) The Institute for Integrative and Multidisciplinary Earth Studies (TIIMES), BEACHON project National Science Foundation, National Center for Atmospheric Research

LIDAR OBSERVATIONS OF FINE-SCALE ATMOSPHERIC GRAVITY WAVES IN THE NOCTURNAL BOUNDARY LAYER ABOVE AN ORCHARD CANOPY

LIDAR OBSERVATIONS OF FINE-SCALE ATMOSPHERIC GRAVITY WAVES IN THE NOCTURNAL BOUNDARY LAYER ABOVE AN ORCHARD CANOPY Tyson N. Randall, Elizabeth R. Jachens, Shane D. Mayor California State University, Chico