Cloud microphysics in the COSMO model: New parameterizations of ice nucleation and melting of snow

1. Axel Seifert1with Carmen Köhler1,2, Claudia Fricke3 and Heini Wernli3 Deutscher Wetterdienst, Offenbach DLR, Oberpfaffenhofen University of Mainz / ETH Zürich Deutscher Wetterdienst GB Forschung und Entwicklung Cloud microphysics in the COSMO model:New parameterizations of ice nucleation and melting of snow

2. Outline of the talk Part 1: Ice nucleation • Motivation • Nucleation processes • Parameterization of homogeneous ice nucleation • Parameterization of hetereogenous ice nucleation Part 2: Melting of snow • Motivation • A melting parameterization with prognostic liquid water fraction Summary and Conclusions

3. Motivation ‘Ice nucleation’ • Research project on the climate impact of contrails and a possible mitigation strategy by ‘environmental friendly flight planning’. • Need to predict regions of contrail formation, i.e. ice supersaturated regions, with the global model GME. • Development of a more advanced microphysical parameterization which is more skillful in predicting ice supersaturation. • The new scheme will be applied in GME and the COSMO model • Possible ‘side effects’ for NWP: • Improved prediction of cirrus clouds (high cloud cover) • Reduce biases in simulated brightness temperatures of clouds (good for data assimilation?)

4. Ice supersaturation in GME (a) Global distribution of RHi in GME (b) In-situ validation of RHi • GME can predict ice supersaturations, but RHi is often too low. • Ice nucleation and depositional growth are probably overestimated

5. Ice nucleation modes Homogenous Freezing Heterogeneous nucleation cloud droplet liquid aerosol particle immersion contact deposition condensation (from a talk by Thomas Leisner, with modifications)

6. Ice nucleation scheme in COSMO/GME Various freezing modes depending on temperature and humidity: • Heterogenous freezing of raindrops: T < 271.15 K and qr > 0 • Heterogenous condensation freezing nucleation: T ≤ 267.15 K and water saturation • Heterogenous deposition nucleation: T < 248.15 K and RHi > 100 % (ice supersaturation) • Homogenous freezing of cloud droplets: T ≤ 236.15 K and qc > 0 For (2) and (3) a number concentration of ice nuclei is assumed: • Very simple, very empirical, based on data for the late 70s! • Homogeneous freezing of liquid aerosols in missing!

7. Cirrus cloud formation: Homogeneous vs heterogenous nucleation • Most cirrus clouds form by homogeneous freezing of liquid aerosols at the critical supersaturation of 150-170 %, depending on temperature • Heterogeneous ice nucleation can modify, and sometimes suppress, homogeneous nucleation. (Ren und McKenzie 2005) • The most important nucleation process for cirrus clouds is missing in COSMO/GME. • Heterogeneous nucleation is probably overestimated and would suppress homogeneous nucleation, if the latter process would be implemented.

8. Kärcher et al. parameterization of homogeneous nucleation Kärcher and Lohmann (2002) developed a parameterization of homogeneous nucleation for atmospheric based the work of Koop et al. (2000). The scheme was further refined by Kärcher et al. (2006) and Kärcher and Burkhardt (2008). • Strong resolution dependency due to Ni ~ w3/2 • The scheme has been implemented in the COSMO two-moment microphysics code. A version the operational scheme with two-moment cloud ice is currently being developed. • GME and COSMO model will be used to investigate scale dependency

9. Phillips et al. parameterization of heterogeneous nucleation Phillips et al. (2008) combined data from various field experiments and laboratory measurements in an empirical parameterization • Currently the ‘best’ scheme available (Eidhammer et al. 2009). • Still large uncertainties in freezing efficiencies/fractions and onset. • Needs additional assumptions about the concentration of dust, soot and organic aerosol particles. Fraction dust particles that freeze at a certain temperature Onset of freezing for soot particles as a function of RH and temperature.

10. Workplan ‘ice nucleation’: still a long way to go….. • Testing of the new nucleation schemes in a parcel model. • Implementation in the two-moment scheme including vectorization on the NEC SX-9. • Development of a ‘hybrid’ scheme based on the operational one-moment scheme. The advanced ice nucleation schemes can only be used with a two-moment cloud ice scheme, i.e., one more prognostic variable. • Testing and application of the new microphysics scheme in GME, COSMO-EU and COSMO-DE. • Development of a sub-grid closure to parameterize the scale-dependency of the forcing, i.e., vertical velocity and temperature fluctuations. • Validation of the new model version with in-situ and satellite data. • Operational use of the new scheme not before 2011.

11. Motivation ‘Melting of Snow’ • Prediction of precipitation phase is a very important problem, especially during winter, e.g., warning of heavy snowfall, freezing drizzle etc. • The direct model output (DMO) is currently insufficient for a skillful prediction of precipitation phase. Post-processing and interpretation is necessary. • The problem for the COSMO model are: • Large-scale dynamics can be wrong. • Temperature- and humidity profiles can be wrong. • Not enough vertical levels to represent the melting layer. • Melting process is oversimplified in the microphysics scheme. • Research project in cooperation with Prof. H. Wernli (Uni Mainz, ETH Zurich).

12. Work hypothesis of the project: • Currently the melted water of snow is immediately transferred to rain (external mixture). • This leads to an overestimation of melting, since the scheme has no memory of the melting stage. • The increase of the fall speed of wet snow cannot be parameterized, and is simply neglected. • The result is a melting layer which is too vertically too thin. This leads to an overestimation of rainfall compared to snowfall. Currently this bias is corrected by post-processing. • Using the melted water on snowflakes as an additional prognostic variable we get the memory effect and can include the wetness dependency of the fall speed of snowflakes.

13. Fall speed of wet snowflakes The transition from dry snowto rain is described by the liquid water fraction: which is 0 for dry snow and 1 for rain. The fall speed of wet snow is the given by: with Ψ(LWF) based on laboratory measurements of Mitra et al. (1990).

14. Parameterization of melting Melting of snow (sink for mi, source of mw ) is parameterized as: with and Note that NRe is a function of vs, i.e. a function of LWF. • Numerical evaluation of the integral, and use of a look-up table might be necessary  but maybe we can find a better solution  • Need an equation for m*, and additional assumptions about the size-dependency of LWF (see Szyrmer and Zawadzki 1999)

15. Workplan ‘melting of snow’: also a long way to go….. • Theoretical work how to parameterize m* and other details. • Development of a new microphysics scheme based on the operational one-moment scheme. • Implementation of the new melting parameterization in the two-moment microphysics scheme • Testing and application of the new microphysics scheme in GME, COSMO-EU and COSMO-DE • Operational use of the new scheme not before 2012

16. Deutscher Wetterdienst GB Forschung und Entwicklung Summary and conclusions • Currently the COSMO model uses very simple empirical (statistical) parameterization for the number of ice particles. • A new microphysics scheme is currently being developed which makes use of new measurements and parameterizations • Currently the COSMO model cannot represent the melting layer very well leading to uncertainties and biases in the prediction of precipitation phase • A new microphysics scheme is currently being developed which uses the liquid water fraction of snowflakes to achieve a better representation of the melting process and the melting layer. • Both project are at the beginning and first results can be expected next year. An operational implementation might be possible 2011 or 2012.

17. Some first results Time-height plots of 1D simulation of melting of snow with a prescribed temperature profile: