EUCLIPSE Toulouse meeting April 2012

1. EUCLIPSE Toulouse meeting April 2012 WP3 – SCM results Mapping the behavior of subtropical marine boundary-layer clouds under weakening inversions Roel Neggers

2. Contents Case update – where are we now? The bigger picture: A further analysis Kappa – TCC diagrams i) Single cases ii) Ensemble SCM: internally resolved composite transitions iii) CMIP5 station data

3. Case update List of participants Short reminder of general model performance A remaining issue in the ASTEX case setup (spinup effects in surface evaporation & low level wind)

4. List of participants New: * Case updates from LMD, NCEP-GFS, ECHAM6 and HARMONIE * CMIP5 physics versions from LMD (AR4) and Met Office (HadGem2)

5. Reminder: General model performance (Exeter meeting) Spaghetti: This looks nasty … should this really be our main message, or can we do something better / more instructive?

6. Remaining issue in ASTEX case Spinup effects in surface evaporation as produced by most SCMs

7. Low level wind speed

8. m m Further analysis in EC-Earth SCM: hodographs

9. Budget analysis of V at 10m:

10. Surface momentum flux (u’w’ + v’w’):

11. Witin the first three hours, the depth of the vertical mixing in momentum is much smaller in most SCMs compared to the LES

12. Q: How can these transition case studies contribute? Q: Are our single case studies representative of GCM climate? Idea: To establish “fingerprints” of relevant fast physics across a hierarchy of models Can we identify similar patterns in GCM and SCM studies? The question: What “key diagnostic” do we choose? Where to go from here? EUCLIPSE goal: attribution of GCM behavior to sub-grid physics

13. Further analysis Kappa-TCC space LES results Lock (QJRMS, 2009) CTEI parameter Kuo and Schubert (QJRMS, 1988) Moeng (JAS, 2000) l qt

14. More LES results “Cloud cover is universally large when  < 0.2 and universally small when  > 0.4 in broad agreement with previous studies. However, there is considerable scatter in the value of cloud cover over intermediate values of  , and our simulations neither support a threshold in cloud cover associated with a critical value of  nor a one to one relationship between  and cloud cover. That cloud cover should generally decrease as  increases is reasonable, as larger values of  imply the entrainment of relatively dry air. The relative scatter however indicates that other processes also play an important role.” Sandu and Stevens (JAS, 2011)

15. Arno Nederlof, Bachelor’s thesis, TU Delft, August 2010 DALES simulations of multiple transitions (variations on ASTEX) Let’s not use  as a threshold Let’s use this PDF as a benchmark that GCMs and SCMs have to reproduce How good are they at this, really?

16. l Calculating kappa in a discretized column Method 0: diagnose  over a single model level at cloud top Method 1: diagnose  over a single model level at the level of the strongest l –gradient Method 2: as 1, but now  is diagnosed over the inversion layer, defined as the height-range in which the l –gradient is still significant (>10% of max) Method 3: as 2, but now correcting for the tropospheric lapse rate in l and qt(downward extrapolation)

17. LES Using Method I Pretty much reproduce earlier results: cloud transition occurs within range 0.1 < kappa < 0.5

18. SCM Method I

19. MetOffice SCM Sensitivity to Kappa-method Two messages: * Use kappa-method that best reflects the way the inversion is treated in the top-entrainment model * Correcting for tropospheric gradients (both qt and l) tends to shift the pdf to larger kappa values. Except in ASTEX, where the tropospheric qt gradient is significant!

20. Ensemble SCM The composite transitions were based on 497 observed/diagnosed transitions in the NE Pacific (Sandu et al, ACP 2010; Sandu and Stevens, JAS 2011 ) Would it not be a great idea to simulate each of the 497 transitions individually? Benefits: * Composites become internally resolved * Increased statistical significance Single realizations might not represent “typical behavior” as seen in GCM E.g. numerical effects like grid-locking at PBL top * More honest comparison to GCM results * This could facilitate the attribution of GCM behavior to sub-grid physics Serving as an intermediate step

21. Dry CBL Deep Conv Day Night A selection from 497 NEP transition cases Simulated with EC-Earth DualM Results: first impression

22. Resolved composite-internal variability EC-Earth (cy31r1) The single realization (composite experiment) is making many excursions In contrast, the ensemble mean is smoothly varying, and shows a weak diurnal cycle

23. Comparison to LES Slow The time-development of the ensemble-mean compares much better to the LES Should we use multiple instead of single SCM realizations to evaluate the representation of this “knife-edge” regime? Reference Fast

24. Kappa-TCC diagrams EC-Earth (cy31r1) EC-Earth DualM Different BL physics produce different PDFs Various modes can be distinguished

25. Conditional sampling Filtering out deep convective events

26. Comparing scatterplots to PDFs Over-plotted: SCM results for ASTEX & composite cases (black symbols)

27. qt Can we see these PDFs as “fingerprints”? Proof of principle: a sensitivity test on a key closure in the PBL scheme Entrainment efficiency at cumulus top (Wyant et al., 1997) In EDMF DualM, the top-level of cumulus mass flux is replaced by K-diffusion, with K parameterized as a function of Me This is necessary to let the scheme represent cumulus rising-into stratocumulus (ATEX) How does the kappa-TCC diagram change when we do not do this?

28. Sensitivity test: result Fair weather cumulus (TCC ~ 20-40 %) occurs much more frequently, at the cost of Strato-cumulus

29. Moving up the model hierarchy: GCM ESM output at selected gridpoints (CMIP5 AMIP)

30. Location : Eastern Pacific ( cfSites 1-29 ) CMIP5 AMIP Period : 200509 - 200608

31. Comparing PDFs The SCMs “kind of” reproduce the PDF as diagnosed from the associated GCM But can we make a qualitative statement?  Come up with a metric? Problem: the SCM PDF is ‘undersampled’: Many situations are not encountered in the SCM cases

32. A simple metric Idea: we can at least compare the location of the mode (maximum) in kappa A benefit: There is no contribution at those n where the (incomplete) SCM-PDF has no samples

33. A cross-comparison of SCMs and GCMs RMS is always smallest for the GCM and its own SCM SCM “best matches” its native GCM

34. Conclusions Ensemble SCM By simulating all 497 individual transitions individually, the composite-internal variability becomes resolved Where cloud parameters in single SCM realizations typically show a lot of random-like excursions, the diurnal cycle is much better manifested in the SCM ensemble mean Kappa-TCC diagrams Seem to be an effective tool for characterizing and inter-comparing the behavior of fast parameterized physics across a hierarchy of simulations In these diagrams, SCM results for transition cases are representative of GCM behavior (attribution) (How) would these diagrams change in future climate?

35. Outlook With the kappa-TCC analysis of single SCM, ensemble SCM and GCM results there is enough material to make an interesting & innovating inter-comparison paper. Things still to do: i) ASTEX spin-up issue  Fix? Reruns required? ii) Forcings and boundary conditions for 497 transitions Available to everyone who is interested in simulating To be put on www.euclipse.eu iii) CMIP5 station data for the remaining three CMIP5 models participating in EUCLIPSE EC-Earth, CNRM (only profiles) and LMD iv) Add kappa-TCC derived from observations? Is this possible (jumps)?