Improvement of Dust Module in CMAQ and Implement of Heterogeneous Chemistry 14 th Annual CMAS Conference 5-7 th October, 2015 Xinyi Dong 1, Joshua S. Fu.

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Improvement of Dust Module in CMAQ and Implement of Heterogeneous Chemistry 14 th Annual CMAS Conference 5-7 th October, 2015 Xinyi Dong 1, Joshua S. Fu 1, Kan Huang 1, Daniel Tong 2,3,4, and Guoshun Zhuang 5 1 University of Tennessee, Knoxville 2 NOAA/OAR/ARL, NOAA Center for Weather and Climate Prediction 3 Center for Spatial Information Science and Systems, George Mason University 4 Cooperative Institute for Climate and Satellites, University of Maryland 5 Department of Environmental Science and Engineering, Fudan University Chapel Hill, NC Oct. 5th, 2015

Outline Brief review of dust emission scheme in CMAQ Implement of model developments Results and discussion

Why we care about dust in CMAQ CMAQv5.0 (Oct. 2012): Wind-blown dust option became available Before : some user-initiated model development studies (Wang et al., 2012, ACP; David et al., 2013, AE) After : application of CMAQv5.0 in US (Appel et al., 2013, ACP) & East Asia (Fu et al., 2014, ACP) Inline mode WRF/CMAQ (Yu et al., 2014, ACP) Hemispheric scale simulation (Jia et al., 2015, ACP) Better predictions of Aerosols Why it’s essential How it’s unique Speciation of dust particles, enable the model to: Chemical evolution of dust particles (CaCO 3, Fe 2 O 3 … ) Change of optical properties Having dust can promote the model to:

Dust Emission Scheme Calculation of dust emission flux (F) is based on modified Owen’s equation (Owen et al., 1964; Tong et al., 2015 ) Ratio between vertical and horizontal flux Soil erodible potential Friction velocity threshold ( if the friction velocity exceeds this threshold, there will be dust particles elevated) Initial friction velocity threshold constant for 13 soil types, based on filed data from Gillette et al. (1980). The values in original CMAQ double count the impact of soil moisture, and our recent reanalysis of the field data provide new values of Soil moisture adjusting factor Surface roughness adjusting factor

Initial Friction Velocity Threshold ConstantsLand Cover Categories Soil Types New Friction Velocity Threshold Constant Taklamakan Gobi Tibet

Source-dependent Speciation Profiles 20% fine mode 80% coarse mode CMAQ default Taklamakan Gobi SpeciesDescription Mass Conributions (%) Fine Mode (I,J mode in CMAQ ≤2.5μm)Coarse Mode (K mode in CMAQ ≤10 μm) DefaultTaklamakanGobiDefaultTaklamakanGobi ACACalcium AMGMagnesium AKPotassium Dust

Heterogeneous Chemistry 13 reactions added into CMAQ in this study: R8 R9 R10 R11 R12 R13 R1 R2 R3 R4 R5 R6 R7 Reaction Rate: : radius of the particle : diffusion coefficient of gas molecules : molecular velocity of gas : particle surface area : uptake coefficient (values from Zhu et al.,2010, ACP) Heterogeneous reactions in original CMAQ: C1 C2

Method Modeling domain and observation stations Simulation design ScenarioConfiguration of WRFv3.3/CMAQv5.0.1 Dust_OffWithout inline calculation of dust Dust_DefaultWith default dust plume rise scheme Dust_RevisedRevised initial friction velocity threshold constant in dust plume rise scheme Dust_ProfileSame as Dust_Revised, but with implemented source dependent speciation profile Dust_Chem Same as Dust_Profile, but with implemented dust chemistry with lower limit of uptake coefficient Dust_ChemHighSame as Dust_Chem, but with upper limit of uptake coefficients Dust_InlineSame as Dust_Profile, but with inline WRF/CMAQ Fudan observation station in Tazhong AERONET API EANET TAQMN Fudan Obs. Simulation period: March-April, Tazhong

Results: Dust Emission PM 10 Bias vs API Default Revised Simulated Dust PM 10

Evaluation Statistics Revised dust scheme significantly improve the model’s performance of reproducing PM 10 and AOD over East Asia, but simulation still contain large uncertainties PM 10 vs API Default Revised AOD vs AERONET AOD vs MODIS

Model Improvement – Trace Metals Default Profile Revised Profile PM 2.5 Model evaluations of fine mode trace metals with Default and Revised speciation profiles at Tazhong, Duolun, and Yulin Taklamakan Gobi Duolun PM 2.5 K+K+ Mg 2+ Ca 2+ DefaultRevisedDefaultRevisedDefaultRevised Mean Obs (µg/m 3 ) Mean Sim (µg/m 3 ) Mean Bias (µg/m 3 ) Normalized MB (%) Correlation Yulin Tazhong

Impact of Dust Chemistry Mean BiasNMB (%)Correlation No_ChemChem_LowChem_HighNo_ChemChem_LowChem_HighNo_ChemChem_LowChem_High O 3 (ppbv) SO 2 (ppbv) SO 4 2- (µg/m 3 ) HNO 3 (ppbv) NOx (ppbv) NO 3 - (µg/m 3 ) with lower uptake coefficientwith higher uptake coefficient Spatial Distribution Evaluation Statistics Against EANET observation O3O3

Case Study: Dust Strom Mar , 2010 MODIS CMAQ (revised) Mar.19 Mar.20Mar.21 Spatial distribution of simulated AOD is consistent with MODIS product

Case Study: Dust Strom Mar , 2010 Default Revised API x AERONET EANET

Uncertainties (1) PM 2.5 mass ration in dust emission CMAQ (PM 2.5 /PM 10 ) Measurement (PM 2.5 /TSP) TazhongDuolunYulin 20%44.6%42.7%39.5% (2) Excessive soil moisture in FNL (Zender et al., 2013; Haustein et al., 2012) Systematic underestimation of fine mode aerosols (1) Overestimation of (2) Underestimation of T, wind speed

Evaluation of Inline WRF/CMAQ Mean Obs(W/m 2 )Mean Sim(W/m 2 )MB (W/m 2 )NMB (%)R TOA Clear sky All sky SFC Clear sky All sky Shortwave radiation flux evaluate against CERES Temperature and Relative Humidity evaluated against MODIS HeightMean Obs (K)Mean Sim (K)MB (K)NMB (%)R T 1000hPa hPa hPa hPa hPa hPa hPa hPa HeightMean Obs (%)Mean Sim (%)MB (%)NMB (%)R RH 1000hPa hPa hPa hPa hPa hPa hPa hPa Mean ObsMean SimMBNMB (%)R T (K) (K)-0.95 (K) Humidity5.36 (g/kg)5.61 (g/kg)0.25 (g/kg) WS3.98 (m/s)4.86 (m/s)0.87 (m/s) WD ( °) ( °) ( °) Evaluate against NCDC observations

Dust Impact in Two-way WRF/CMAQ Dust induced Shortwave Radiation Change Surface Compare with other studies RegionMethod Dust induced TOA (W/m 2 ) Dust induced SFC (W/m 2 ) South Korea CCM3 (Won et al., 2005)5 ~ ~ -10 This study5 ~ ~ -10 East China CCM3 (Park et al., 2005)10 ~ ~ -10 ADAM (Park et al, 2008)--40 ~ -20 This study5 ~ ~ -20 Tibet Plateau MFRSR (Ge et al., 2011)-4 ~ 3-20 ~ -10 fvGCM (Lau et al., 2006)-2-13 WRF-Chem (Chen et al., 2013)-4-6 This study-30 ~ 5-10 ~ -60 Taklamkan RTM, CALIPSO (Kuhlmann and Quaas, 2010) --60 ~ -90 This study0 ~ 5-20 ~ -40 Snow cover (NHSNOWM) Hengduan Mountains

Summary Preliminary Conclusions: Revised friction velocity threshold constants and source- dependent speciation profiles help to improve the model predictions for PM10, AOD and trace metals Large discrepancies still remain due to possible uncertainties from: dust speciation, and soil moisture Inline mode indicated strong cooling effect of dust particles at surface, especially over snow cover areas Next steps: Examine dust particle’s evolution through chemistry with CaCO 3 and Fe 2 O 3 Sensitivity studies to eliminate the uncertainties within fine particles ratio and soil moisture

Acknowledgement We acknowledge Dr. David Wong for his help on reposing technical questions, and NSF National Institute for Computational Sciences (NICS) at the University of Tennessee for providing the computer sources for model simulations of this research.

Thanks !