World Meteorological OrganizationWorking together in weather, climate and water

ACTIVITIES OF THE BELGRADE DREAM MODELLING GROUP IN THE PERIOD 2012-2014

G. PEJANOVIC, S. NICKOVIC

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 http://www.seevccc.rs/GMINER30/

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

0

TSK

dt

dS

How to model K ?

K from GMINER30

F/T ratio from GMINER30

%1.0;100

/1.222.84ln

10

0

s

s

TF

tK

f

Markers: sampling sites (Shi et al. 2011)

GMINER30 F/T Fe ratio

Sam

ple

d F

/T F

e ra

tio

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

http://aerosoli.com/

21 Aug

20 Aug

22 Aug

Tenerife, MPLModel

23 Aug

Model Tenerife, 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

01May 03May 07May 09May 11May 13May 15May05May

01-04 06May 10-13 07May02-05 07May 13-15 07May18-02 06May09-12 06May

01-03 08May20-23 07May16-19 07May 03-06 08May 06-09 08MayRaman lidarCloud 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 areasthat 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-DREAMNMME – Non-hydrostatic Mesoscale Model on the E-grid (NOAA/NCEP)DREAM – Dust REgional Atmospheric Model

•horizontal resolution: 3.7 km

•start: July 5th, 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. 2004.)

Haboob dynamics

36 International SDS Workshop, Teheran, Iran, October 2011

7:45 PM Phoenix as the dust storm neared.

Phoenix (Arizona) Haboob, 5 July 2005

MCD12Q1

barren land cover2009 vs. 2005

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 daysselected non-vegetated areas with NDVI < 0.1for open shrubland category:

NDVI < 0.1 100 % bareNDVI 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 ledto this high-resolution forecast &simulation capability

Successful simulation of the Phoenix haboob(Chapman University dust modelling group)

Phoenix

Phoenix

NMME-DREAM PM10 dust concentration vertical cross section

1500 m 1500 m

1500 m 1500 m

Observed and modeled PM10 for 11 Maricopa measuring stations

Thank you