Pier Siebesma

1. The Grey Zone Project Grey Zone committee: Andy Brown, Jeanette Onvlee, Martin Miller, Pier Siebesma • Introduction on the Grey Zone (from a parameterizational point of view) • A proposal (thanks to Paul Field and Adrian Hill (Met Office)) • Discussion and Next Steps Pier Siebesma

2. Motivation • Increased use of (operational) models in the “grey zone” (Dx = 1 ~10km) • Models operating in this resolution range resolve some of the “aggregation of convective cells” but certainly no individual convective cells. • This leads to the “wrong” perception that these “grey-zone” models, when operating without (deep) convection parameterizations, can realistically represent turbulent fluxes of heat, moisture and momentum. • Hence there is a urgent need of a systematic analysis of the behavior of NWP models operating in the “grey-zone”: • “The Grey Zone Project”

3. Some Background “Many of the parameterization issues (especially clouds and convection) evolve around finding the subgrid (co)variances of w, T and q”

4. Prime Example: Cloud schemes “Many of the parameterization issues (especially clouds and convection) evolve around finding the subgrid (co)variances of w, T and q”

5. qt qsat Prime Example: Subgrid Cloud Fraction “Many of the parameterization issues (especially clouds and convection) evolve around finding the subgrid (co)variances of w, T and q”

6. qsat(T) . (T,qt) qt T Prime Example: subgrid cloud fraction “Many of the parameterization issues (especially clouds and convection) evolve around finding the subgrid (co)variances of w, T and q”

7. qsat(T) . (T,qt) qt T Prime Example: subgrid cloud fraction “Many of the parameterization issues (especially clouds and convection) evolve around finding the subgrid (co)variances of w, T and q” Cloud fraction:

8. qsat(T) . (T,qt) qt T Prime Example: subgrid cloud fraction “Many of the parameterization issues (especially clouds and convection) evolve around finding the subgrid (co)variances of w, T and q” Cloud fraction:

9. This biased errors slowly go away if resolution increases: • Typically allowed for Dx~100m, i.e. at the LES scale • So for all models operating at a coarser resolution additional information about the underlying Probability Density Function (pdf) is required of temperature, humidity (and vertical velocity). • Or, as a good approximation, the variances and covariances: As a function of the used resolution!!

10. Example:a posteriori coarse graining • Use a reference (LES) model that resolves the desired phenomenum ( Dx << lphen) • Has a domain size much larger than the desired phenomenum ( L >> lphen) • Coarse grain the (co)variances across these scales subgrid resolved

11. Methodology: • Use a reference (LES) model that resolves the desired phenomenum ( Dx << lphen) • Has a domain size much larger than the desired phenomenum ( L >> lphen) • Coarse grain the (co)variances across these scales subgrid resolved l = L l = Dx

12. Applied to shallow cumulus convection: Subgrid contribution Subgrid contribution starts to shrink if l~ 5 lphen~5km l

13. Similar analysis for fluxes (heat flux) Grey zone sets in at smaller scales Due to the fact that the w-spectrum peaks at smaller scales

14. Not completely unrelated to stochastic physics……….. subgrid resolved l=1.6km l=6.4km l=12.8km Moisture turbulent flux

15. Stratocumulus generates larger horizontal scales……. How large is large enough? De Roode et al: JAS 61 p403 2004

16. Subgrid contribution

17. Subgrid contribution Now the grey zone sets in at 10 km or more.

18. Schematics of the grey zone (including the stochastic arena) • Remarks: • The grey zone depends on the size of the considered process (convection (shallow or deep) , clouds (stratocumulus, shallow or deep) • The stochastic arena is stretching out a factor of 5 to 10 further. Deterministic (statistical) Arena Stochastic Arena Grey zone variance, flux total parametrized resolved Lphen 10~50 Lphen Horizontal resolution (Dx)

19. Proposal (from WGNE 2010 meeting) • Project driven by a few expensive experiments (controls) on a large domain at a ultra-high resolution (Dx=100~500m) (~2000x2000x200 grid points). • Coarse grain the output and diagnostics (fluxes etc) at resolutions of 0.5, 1, 2, 4, 8, 16, 32 km. (a posteriori coarse graining: COARSE) • Repeat CONTROLS with o.5 1km, 2km, 4km, 8km, etc without convective parametrizations etc (a priori coarse graining: NOPARAMS) • Run (coarse-grain) resolutions say 0.5, 1km, 2km, 4km and 8km with convection parametrizations (a priori coarse graining: PARAMS) • Preference especially from the mesoscale community for a cold air outbreak

20. Aims • Show how faithfully fluxes, variances, cloud structures, etc can be represented by comparing COARSE, NOPARAMS and PARAMS depending on all aspects of set-ups. • Guide improvements in current schemes especially at these resolutions - essential for future progress • Gain some insight and understanding of what can be achieved without parametrizations • Clarify what cannot/should not be done without parametrization also!! Strong Support from both the international NWP and Climate community

21. CONSTRAIN – proposal for cold air outbreak case(thanks to Paul Field & Adrian Hill, Met Office)

22. CONSTRAIN • flights over the North Atlantic from January 12 to 31 2010. • The proposed case is based in observations and NWP data from January 31st 2010. • Observations show that this day is characterised by northerly flow and stratocumulus clouds at 65N -10W. As air advects over warmer seas the Sc transitions to mixed-phase cumulus clouds at around 60N, prior to reaching land

23. The Case (1) • The Mesoscale Community is interested to start with an extra-tropical case • Cold-air outbreaks are of general interest for various communities • Proposal: “Constrain” cold-air outbreak experiment • 31 January 2010 • Participation of global models, mesoscale models but also from LES models !! • Domain of interest: 1500X1000 km • Quick Transition : ~ 12 hours

24. The Case (2) 4 Different Flavours • Global Simulations (at the highest possible resolution up to 5 km) • Mesoscale Models (Eulerian) • At various resolutions (up to ?) • Mesoscale Models (Lagrangian) • Idealized with periodic BC • highest resolution (~100m) • LES models (Lagrangian) • (in the same set up as 3)

25. LAM simulation Quasi-lagrangian trajectory Low cloud fraction

26. Quasi-lagrangian timeseries start point 65 N -10 W, 0 UTC

27. Initial Proposal for LES/Mesoscale Langrangian set-up • x,y domain = 250 X 250 km (how large is large enough? ) • dx, dy ~ 250 m • z domain = 5 km • dz = 20 m between surface up to 100m at 5000m • The case is initialised with total water, potential temperature based on output from the NWP simulation

28. Forcing Surface forcing Subsidence x hours spin-up with fixed SST

29. Interest on participation on the Grey Zone Project

30. Discussion Points (1) • Global Model runs: • The cloud types under consideration are probably to small to be able to penetrate into the grey zone (at least for the turbulent fluxes) • So for these models it is more a classic parameterization excerise • Still relevant enough? • Look for (larger) cloud types for which the grey zone issues are more relevant? • MJO case , now? In a later phase? • Eulerian (operational) Mesoscale Model runs • Can some of these models reach the fully resolved mode “LES (~250m) implying a domain size of 1000X1500 km ? (4000X6000 grid points) • Will each mesoscale model run with the same lateral boundary conditions?

31. Discussion Points (2) • Lagrangian runs (LES and Mesoscale) • How large is large enough? (250X250km) • How long do we need to spin up in order to let mesoscale structures form • How much do we constrain/simplify the case in the Lagrangian mode: • simplify subsidence fields? • Prescribe drag coefficients for the surface fluxes? • Nudge winds?

32. Time Line and Organisation • october/december : testruns with at least 2 LES (MOLEM, DALES) • Release January 2012 Organisation: different case leaders for the 4 flavours ( a la TWP-ICE)? MPI has shown interest (Verena Grutzun) Meto? Others?