World Meteorological Organization Working together in weather, climate and water ACTIVITIES OF THE BELGRADE DREAM MODELLING GROUP IN THE PERIOD G. P EJANOVIC, S. N ICKOVIC South East European Climate Change Center (SEEVCCC), Republic Hydrometeorological Service Belgrade,, Serbia SDS-WAS RSG Meeting, Castellaneta Marina, Italy, 6 June, 2014

Highlights Assimilation Mineralogy Dust-cloud interaction High-resolution modelling

Assimilation

For a selected dust intrusion into Europe assemble EARLINET lidar profiles from Munich, Aberystwyth, Barcelona, Leipzig, Neuchatel objective analyses of lidar dat with a successive correction method. mixing lidar profiles and predicted concentration; Bscat coefficients  mass concentration Ansmann et al. (2003) Early attempts (2002) at assimilation of EARLINET data in DREAM

DIFFERENCE: (ASSIM-NOASSIM) 2 km concentration (  g/m^3)‏

DREAM8 –8 particle size version –Operational assimilation from February 2010 at SEE-VCCC (Belgrade) –ECMWF daily MODIS aerosol assimilation used as a background field Assimilation of dust aerosol in the SEEVCCC-DREAM8 model (2010)

DUST OPERATIONAL FORECAST SYSTEM WITH ASSIMILATION OF SATELLITE AEROSOL OPTICAL DATA Nickovic et al, 2012

Assimilation: plans of the Belgrade DREAM group In collaboration with ACTRIS/EARLINET (Potenza IMAA-CNR; NOA/Athens group; Bucharest, …) to perform experiments with ingested lidar observations to combine lidars with assimilated Satellite AOD

Dust mineralogy dataset

10 Why mineralogy of dust is important? Fe and P embedded in dust  ocean nutrients Cloud ice nucleation (IN) sensitive to dust mineral composition; Breaking news: Atkinson et al 2013, Nature: Feldspar by far most efficient IN Radiation absorption/reflection depends on dust colour Fe as an enhancement factor in meningitis outbreaks (Thompson, 2008) and in bacterial infections, in general

DREAM-Fe model: Use of the new 1 km global mineralogy database in a dust-Fe regional model A new dust-Fe regional model based on DREAM model Parameterization of Fe solubility as a function of dust mineralogy Simulations for several Atlantic cruises

GMINER30 database Mineralogy database - a precondition for studying Fe atmospheric transport 1 km global 9 minerals in arid soils Data used: –FAO soil types (4km) –USGS land cover (1km) –STATSGO textures (1km) –Claquin et al (1999) table (minerals vs. soil types) Nickovic et al., (2012), ACP GMINER30 available at

Geographic distribution of: a)Quartz, b) Illite, c) Kaolinite, d) Smectite, e) Feldspar, f) Calcite, g) Hematite, h) Gypsum and i) Phosphorus

Iron in dust – transport and deposition to ocean

Most Fe modelling studies assume 3.5% Fe in sources 1-km Fe fraction (%) - a missing puzzle in dust-Fe models is now available; Fe – spatially distributed

ATMOSPHERIC IRON PROCESSING AND OCEAN PRODUCTIVITY

Iron forms in aerosol structural iron embedded in the crystal lattice of alumino-silicates referred as ‘‘free-iron’’; oxide/hydroxide iron referred as ‘‘iron oxides”. (Lafon et al., 2004) Journet et al. (2008) showed that mineralogy is a critical factor for iron solubilization.

Tracers in DREAM-Fe Emission, advection, vertical mixing, wet/dry deposition Tracer concentration equations – dust (C) – total Fe (T) – free Fe (F) – soluble (S) Fe chemical transformation: first order reaction kinetics How to model K ?

K from GMINER30 F/T ratio from GMINER30 Markers: sampling sites (Shi et al. 2011) GMINER30 F/T Fe ratio Sampled F/T Fe ratio

Total Fe Free Fe Fe solubility

Dust and cold cloud generation

Ice nucleation (IN) and role of dust/mineralogy More than 60% of clouds start as cold clouds A key climate and weather factor Aerosol impact on clouds one of least known processes (IPCC) Lidars, cloud radars – important source of information for aerosol and clouds Initial work in collaboration with –IMAA-CNR Potenza –ETH –AEMET (Izana Observatory)

Heterogeneous cloud freezing

IN parameterizations in DREAM IN - a function of dust C, T and moisture Parameterizations tested: –Niemand et al (2012) –DeMott (2010)

Physical and mineralogical features of Saharan dust over Eastern Atlantic: Experiment simulated by DREAM dust model model-simulated physical and chemical features of Saharan dust transported towards Canary Islands, DREAM extended with a new prognostic parameters as tracers –: Illite and kaolinite; feldspar; calcite; # ice nuclei (IN) IN calculated using DeMott et al (2012) empirical parameterizations. DREAM model - horizontal resolution 25km. support of the CALIMA (Cloud Affecting particles In mineral dust from the Sahara) 2013 field campaign conducted by ETH Zürich, Switzerland and Izaña Atmospheric Research Centre, AEMET, Spain.

August 2013 Canaries field experiment – DREAM simulation outputs

21 Aug 20 Aug 22 Aug Tenerife, MPLModel

23 Aug ModelTenerife, MPL

Preliminary work on comparing model vs Potenza obs (lidar, cloud radar) Raman lidar –Advantage: detecting both clouds and dust –Disadvantage: short periods of obs time Ka-band cloud radar (MIRA-35) –Advantage: continous obs of cloud structure –Disadvantage: no dust detected

01May03May07May09May11May13May15May05May May May May May May May May May May May May Raman lidar Cloud radar

High-resolution modelling

Challenges: convective storms with strong vertical movements potential dust sources in the SW US are mainly local, dust sources in the SW US can be seasonal, from cropland and other areas that don’t have vegetation due to agricultural practice or drought conditions High resolution numerical simulation of the dust event Numerical simulation set up: coupled atmospheric-dust regional model NMME-DREAM NMME – Non-hydrostatic Mesoscale Model on the E-grid (NOAA/NCEP) DREAM – Dust REgional Atmospheric Model horizontal resolution: 3.7 km start: July 5 th, 2011 at 00 UTC ; forecast for 48 hours ; hourly outputs mask of potential dust sources created using MODIS satellite data Vukovic et al., 2014, Atmos. Chem. Phys.

Cross-section of a thunderstorm creating an outflow boundary and haboob (Source: Desert Meteorology. Thomas T. Warner ) Haboob dynamics

36 International SDS Workshop, Teheran, Iran, October :45 PM Phoenix as the dust storm neared. Phoenix (Arizona) Haboob, 5 July 2005

MCD12Q1 barren land cover 2009 vs gray: both barren yellow: 2005 barren 2009 not barren red: 2005 not barren 2009 barren

Dust sources mask (bare land fraction) on NMM-DREAM resolution of 3.7 km Mask of potential dust sources Land Cover Data – annually updated selected types that can be dust productive: barren or sparsely vegetated, cropland, natural vegetation, open shrubland NDVI Data – updated every 16 days selected non-vegetated areas with NDVI < 0.1 for open shrubland category: NDVI < % bare NDVI from 0.11 to 0.13 fraction of bare soil decreases linearly from 70 % to 30 %.

39 International SDS Workshop, Teheran, Iran, October 2011 DUST SIMULATION – 6-km model 10m WIND MAGNITUDE W.A.Sprigg, S. Nickovic, G. Pejanovic, A. Vukovic NASA Applied Science support led to this high-resolution forecast & simulation capability Successful simulation of the Phoenix haboob (Chapman University dust modelling group) Phoenix

NMME-DREAM PM10 dust concentration vertical cross section 1500 m

Observed and modeled PM10 for 11 Maricopa measuring stations

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