Cold cloud formation due to dust: operational prediction of ice … · Cold cloud formation due to...

Cold cloud formation due to dust: operational prediction of ice nuclei concentration

S. Nickovic1, B. Cvetkovic1, F. Madonna2, Marco Rosoldi2, G. Pejanovic1, and S. Petkovic1, J. Nikolic1

1Republic Hydrometeorological Service of Serbia (RHMSS), Belgrade, Serbia2Consiglio Nazionale delle Ricerche, Istituto di Metodologie per l’Analisi

Ambientale, Potenza, Italy

The Atmospheric Ice Nucleation Conference, 23-25 Jan 2017, Leeds, UK

Ice nucleation (IN) and Numerical weather Prediction (NWP)

Cold clouds – especially poorly described in NWP. Why? Since recently – not fully unclear which aerosol types are most

important for IN

Ice nucleation (IN) concentration prescribed as a constant or a climatology used

→ Missing sufficiently correct aerosol-cloud interactions in operational NWP models

climate models

Uses dust model climatology to parameterize IN

Thompson and Eidhammer (2014) dust frendly cloud physics scheme

‘Cooking’ cold clouds: our recipe

Nickovic et al, 2016, Atmos. Chem. Phys., 16, 11367–11378

DREAM modelNickovic et al 2011)

NCEP/NMM model

Dust C T, RH

INn

Thompson and Eidhammer (2014) dust-friendly cold cloud micrphysics

How much DREAM dust C is accurate?

Agia Marina (Cyprus) April 2016: DREAM dust C (upper) vs. Lidar depolarization ratio

What dust does to cold clouds??

Breakthrough in understanding the role of dust in IN process

• Cziczo et al., Science (2013)

Heterogeneous IN dominant (95%)

Dust in 2/3 ice crystals

Sampling done 1000-s km far from dust sources

Cziczo et al, 2013, Science

How to exploit this findings in NWP?

Routine prediction of ice nucleation: DREAM model

• DeMott (2015) за [-35oC <T<-5oC]

• Steinke et al (2015) за [-55oC <T<-35oC]

Дан РХМЗ, 27. септембар 2016.

TT

dustIN nCn16.27316.273

exp

%10051088.1

iceRHqpT

dustIN eSn

New IN parameterizations

IN concentration due to dust ( )in cloud schemes

• Typical today’s cloud schemes use:

or

climatology

INn

constnIN INn

Vertical distribution - Model #IN (color)vs.- MIRA55 Ice Cloud Water(black line)

May 2010 dust case - Potenza

0 2 4 6 8 10

0

1

2

3

4

5

R=0.83

1-14 May 2010 + 22-30 Sep. 2012

NL

IWP

Validating #IN parameterization

• Model runs: May 2010 and Sep 2012

Nickovic et al, 2016, Atmos. Chem. Phys., 16, 11367–11378

0 2 4 6 8 10

0

1

2

3

4

5

R=0.83

1-14 May 2010 + 22-30 Sep. 2012

NL

IWP

Model vs. Cloud radar/lidar Ice Water Content (IWC) observations (Potenza)

model dust load

model loadlog10(nIN)

MSGSEVIRI log10(IWP)

log10(nIN) /log10(IWP)overlaps

1 May

2 May

3 May

4 May

5 May

Model vs. MSG SEVIRI Ice Water Path(May 2010)

Daily #IN maps athttp://dream.ipb.ac.rs/ice_nucleation_forecast.html

NWP groups interested to use daily #IN DREAM forecasts will have it available through the WMO SDS-WAS dust project (ongoing action)

Practical benefits of predicting dust-IN

Solar power forecasting... Soret et al, 2016

Failure to predict cold clouds over Germany 4 April 2014, since no dust-IN effects are considered

DREAM-IN model rerun (left) and SEVIRI cloud phase observation (right)

4 April 2014

AVIATION

AirFrance 2009 accidentHypothesis on dust influence: dust-icenucleation

Thank you!