From “dynamical thinking” to parametrization progress…?

1. From “dynamical thinking” to parametrization progress…? Steve Derbyshire Thanks to: Sean Milton, Martin Willett, Rachel Stratton, Hongyan Zhu

2. From dynamical thinking to (faster) parametrization progress? Contents • What limits our progress? • Illustrations of our experience • Perspectives

3. 1. What really holds us back?

4. The Advance of Science Source:Anonymous

5. Perhaps we’re limited by... • Detailed convective process knowledge? • But NB lots more obs, CRMs etc.. • Capabilities of non-cloud resolving systems? • Much-rehearsed arguments • Cf. MetO involvement in CASCADE (~1.5km but aim to drive conventional parametrization improvements too) • Difficulties in mapping between process study and “full model” results? ?

6. 2. Illustrations of our experience

7. Illustrations in NWP case-study sample Known problems identified in systematic NWP verification (~ 2005) illustrated in small sample used for preliminary testing of changes: • Humidity biases • Temperature biases • Excessive tropical circulations (Following plots from Sean Milton/Martin Willett)

8. Relative Humidity biases in 5-day forecast tests • Substantial dry bias in inner-tropical upper troposphere (up to 10% RH in places)

9. Temperature biases in NWP 5-day forecast • Warm bias in most of tropical troposphere but cold bias at the top of convection (top too low?)

10. Wind spin-up in 5-day forecasts

11. Precipitation and wind spinup S.Milton

12. Expectations for convection parametrization sensitivities Simple arguments: • T, q biases seen in NWP and SCM results must have some impacts on dynamics and hydrological cycle • E.g. 0.5K convective warm bias cf. SST+0.5K? • Weak Temperature Gradient arguments • Various other impacts • convective momentum transport • cloud-radiative effects

13. Total heat and parametrization sensitivity Total heat thinking (Emanuel-Neelin): tropical convection broadly response to accumulation of h=cpT+Lq+gz, exported largely via Possible explanations for excessive convection: Convective updraught Convection exports insufficient h for given mass-flux (cf. Neelin’s Gross Moist Stability)? Cloud layer Precipitating downdraught DD too weak? Or carry insufficiently low h back into the BL (because saturated)? (cf. Raymond) BL Winds too strong? ch too high? Sea too warm? (cf. Emanuel) Surface transfer ~ ch U Dh

14. EUROCS humidity case/motivation for adaptivity Mass flux profiles in a range of free-tropospheric humidities Cloud-Resolving Model SCM (UM4.5) 90% 70% 50% 25% Derbyshire et al., QJRMS (2004) – EUROCS Special Issue [Numbers are for “humidity parameter RHt”]

15. Specific changes Adaptive forced detrainment as extension of Gregory-Rowntree forced detrainment • Replace 0.2K threshold by more physical scaling so fractional detrainment ~ fractional decline in buoyancy

16. Adaptive Detrainment – Relative Humidity impacts (Preliminary NWP tests by Sean Milton/Martin Willett)

17. Adaptive Detrainment – Temperature impacts

18. Observations Preliminary climate tests – J.Rodriguez 1-year annual mean precipitation (1990) Adaptive mod seems to improve Tropical W. Pacific with benefits to Pacific wind-stress HadGAM1 control HadGAM1 with adaptive detrainment

19. Impact on wind-stresses (Rachel Stratton)

20. 3. Perspectives

21. What we found We corrected much of the T, q biases using an improved convective detrainment that we wanted to implement anyway This also seemed largely to improve the excessive broadscale convection circulations Particular benefit to Pacific wind-stresses (and hence ENSO): robust ~30% reduction All broadly consistent with WTG thinking (which was part of the original motivation) But.. more mixed signals with e.g. MJO (ongoing work including H.Zhu, H.Hendon)

22. Qs to dyn-conv community Was this a good use of WTG thinking (simple, impressionistic..)? How much consensus is there anyway about these feedbacks? What about other (e.g. rotational) modes? Handle v as well as u? Can the community do more to reach a truly consolidated understanding? Can/should parametrization developers more systematically harness dynamical thinking to accelerate progress?

23. Questions and answers

24. Supplementary slides

25. Adaptive entrainment and detrainment In CRM study of deep convection Swann (QJ 2001) found: Plume excess buoyancy ~ half adiabatic value adiabatic (no entrainment) height actual (schematic) 0 Plume buoyancy excess qvex Seems to imply entrainment and detrainment adapt to control qvex

26. Impact of shallow-Cu “microphysical” tests • (1) “No shallow” test: treat as deep convection • (2) “Shallow precip” test: reduce precipitation threshold from 1 g/kg to 0.5 g/kg • Similar impacts in NWP tests • Seems to explain some past experience with convection changes via side-effects on shallow-Cu precipitation - + + latitude (A.Lock; similar impact shown in aquaplanet by J.Petch)

27. NWP tests of shallow-Cu precip change Adrian Lock, using NWP Case Study Suite

28. Slides from Australian UM partners (Zhu, Hendon, CAWCR)

29. Mean daily precipitation composited into 5 % bins of saturation fraction SP-CAM OBS CAM UM Extension of Zhu et al. (JAS 2009). UM here is a version of UM6.3 (first attempt at a CAWCR version)

30. NEW RESULTS! May 2009 with UM6.6 HadGEM3-A job agtgd Fig.1

31. UM-6.3 UM-6.6 Fig. 3

32. UM-6.3 UM-6.6 Fig. 4

33. Hongyan’s summary • One year climate run analyzed with UM 6.6 • Relationship between precipitation and saturation fraction has been improved greatly comparing with the results from UM 6.3 (see Fig.1). According to the exponential relationship between precipitation and saturation fraction, I anticipated that the MJO should be better in UM6.6. • I used the UM 6.6 10-year climate run from my college, Zhian Sun, who is working on radiation in ACCESS. The coherence spectrum between symmetric precipitation and U850 (Fig.2) shows that there is peak coherence for UM6.6 at wavenumber 1 with about 50 days period, consistent with the observed MJO. The symmetric U850 space-time power spectral for U850 (fig. 3) also exhibits a strong peak at wavenumber 1 and 50 periods, a good agreement with observation. For the precipitation (Fig.4), the power spectral peaks at wave number 2 and 3, but in the rather low frequency region (bigger than 50days). Even though, the eastward power is stronger than the westward power in UM 6.6, which was about the equal in UM 6.3 run. Observed MJO is characterized by a distinct spectral peak at eastward wavenumber 1-3 with period of 30-90 days for precipitation. • Timing of latent heat flux cf. precip also improved (role of BL changes?)