Dust aerosols in NU-WRF – background and current status Mian Chin, Dongchul Kim, Zhining Tao

I am going to talk about: GOCART dust scheme Comparisons of GOCART dust with observations Dust simulation in NU-WRF – past and current development

Dust emission Dust aerosol simulation in NU-WRF started with the GOCART emission scheme developed by Ginoux et al., 2001, which was implemented into the WRF-Chem by Georg Grell/Steve Peckman/Thomas Diehl about 7 years ago Dust emission flux F for dust size bin with effective radius r in GOCART is calculated as F(r) = C S s(r) u 10 2 (u 10 2 – u t ) where C is a dimension constant, S is the “source function” that is determined by the degree of topographic depress (0-1), s(r) is the fraction of each size class emitted, u 10 is the 10-m wind velocity, and u t is the threshold wind velocity, which is determined by particle size, density, and surface wetness w (= soil moisture / porocity). W functions like a switch: no dust emission when W is above a chosen threshold S is considered as the uplifting probability or erodibility Five size bins, 1 clay (r =0.1-1 μm) and 4 silt (1-1.8, 1.8-3, 3-6, and 6-10 μm)

Current GOCART – In previous version and the version implemented in WRF- Chem, S is a static function because of the surface vegetation coverage is from a static, “climatological” dataset (Ginoux et al., 2001) Recent improvement in GOCART is to use the AVHRR NDVI data to construct a dynamic source function where S is determined by (1) topographic depress, (2) surface bareness estimated with the satellite NDVI values (Kim et al., 2013) to account for the seasonal and interannual variability of dust source, and (3) other conditions (e.g., snow cover, soil depth, surface type) We chose a “bareness” criteria with NDVI ≤ 0.15 Biweekly AVHRR NDVI from 1981 to 2008, and climatological monthly AVHRR NDVI

NDVI over North Africa Bareness criteria: NDVI ≤ 0.15

Aerosol Optical Depth, OMI AOD GOCART AOD OMI AOD MODIS AOD From GOCART: Global 2010 average at 550 nm: AOD=0.11, with 37% from sulfate, 33% from dust, 14% from sea salt, 12% from POM, and 4% from BC

Regions (color) and AERONET sites (circles)

Comparisons with AERONET sites in dust source regions – Middle East × AERONET Δ OMI SU BC POMDust Seasalt GOCART

Comparisons with AERONET sites in dust source regions – North Africa × AERONET Δ OMI SU BC POMDust Seasalt GOCART

Comparisons with AERONET sites in dust source regions – Central and South Asia × AERONET Δ OMI SU BC POMDust Seasalt GOCART

Relationship between emission, 10-m winds, and ground wetness in different dust regions The change of dust emissions in the Sahara and Sahel responds mostly to near-surface wind speed changes, but in Central Asia, ground wetness is the primary influence. In the Middle East, both wind and ground wetness play important roles in regulating dust emissions E = C S s(r) u 10 2 (u 10 – u t ), u t = f(r, w ) where w = ground wetness

Fine mode dust concentrations over the US – Comparison with IMPROVE data 2010 ACAD1 (44.4N, 68.3W) ROMO1 (44.3N, 105.5W)

Remarks regarding GOCART dust performance based on Ginoux method On the relatively large spatial and temporal scales (daily, monthly, annually, multi-year), dust simulated with GOCART is satisfactory in major dust source regions and areas affected by transport. Our issue is the dust optical properties However, the same parameterization may not work with regional models focusing on much smaller spatial and temporal scales (e.g., minutes to hourly) and on locations close to the actual source For such occasions, especially if the regional model generated meteorology is incorrect at those locations at that time, the simulations can be problematic Concerns raised by others: – Using 10-m winds is not physical – Constant clay and silt fractions are not realistic

Dust simulation with NU-WRF Advantage with NU-WRF: Land-atmosphere-aerosol- cloud-meteorology-radiation coupling, allowing interactive processes to study interactions and feedbacks at high spatial and temporal resolution Case study (right): differences in dust emissions with different land cover data (Tao et al., 2013). Dust emission was calculated with GOCART scheme used Ginoux static source function Changes of dust emission due to the land cover effects on meteorology Dust emission (kg km -2 h -1, 3 May-30 June 2010, with USGS land cover data Difference in dust emission between using UMD and USGS land cover data Difference in dust emission between using MODIS and USGS land cover data (Figure from Tao et al., 2013)

Transport effects on US air quality NU-WRF model simulation of surface PM2.5 over the US: GOCART aerosol coupled with Goddard cloud microphysics and radiation modules, with global initial and boundary conditions, Apr-Jun 2010 (Tao et al., manuscript in preparation, 2014) Change of PM2.5 due to direct transpacific transport of aerosols (no cloud and radiation feedback) Change of PM2.5 due to the change of meteorology from transpacific transport of aerosols (with feedback) Change of ozone due to the change of meteorology from transpacific transport of aerosols (with feedback) Transpacific transport of aerosols affect US air quality through directly supplying additional aerosol sources, and indirectly via modifying meteorological fields from the interaction with clouds and radiation

Recent developments of NU-WRF dust simulation capability Creating global 1-km spatial resolution of dust topographic depression map using Gionux’s method Using MODIS vegetation data (GVF or NDVI) to determine the surface bareness dynamically (monthly variation) Using AFWA option that is based on Marticorena and Bergametti (1995) Coupling with LIS (dust emission affected by the change of soil moisture and meteorology)

High resolution dynamic dust source Dust source area and erodibility Dust emission winds wetness Topographic depression SPoRT MODIS NDVI July 2011 SPoRT MDOIS NDVI ≤ 0.15 July 2011 Area with NDVI <=0.15 (Dongchul Kim)

Case study: Arizona dust storm, July Dongchul Kim and Zhining Tao, this afternoon Nested simulation (finest domain 1-km resolution), no LIS coupling, GOCART vs. AFWA scheme Single domain simulation (1-km), GOCART scheme, without LIS vs. with LIS coupling Comparison with global model simulations

Dust concentration (bin 3) (ug/kg-dryair) on 7/6/2011 Without LIS With LIS , 01 Z , 02 Z