Parameterizing convective organization a stab, in current CAM and why

1. Parameterizing convective organization a stab, in current CAM and why Brian Mapes and Richard Neale

2. Parameterizing convective organization a stab, in current CAM and why Brian Mapes and Richard Neale

3. What did the movie show? • (I claim, for current purposes): • Afternoon timing of rain set by an organization delay time • This is many cu parcel turnover times • Aided by mtns in this case • More generally, aided by precipitation • (via its evaporation - cold pools • a positive feedback • When it rains, it pours

4. Outline • Several more slides emphasizing concept and pervasiveness of ”organization” • What this study isn’t about, and is • Sensitivities and feedbacks • WHAT WE DID TO CODE • WHAT IT DID TO MODEL OUTPUTS • Discussion type stuff and summary

5. A model view of diurnal development Khairoutdinov and Randall 2006high-res simulations of shallow-deep transition (flat perioidc domain, specified diurnal surface flux)

6. Khairoutdinov and Randall 2006Organization (“upscale growth”) takes time

7. precip. evaporation is key Khairoutdinov and Randall 2006

8. Mesoscale org. is ubiquitous in deep convectionCloudsat: an unbiased sample from the Asian monsoon connected cloud objects sorted by top heighttrue aspect ratio

9. Mesoscale org is ubiquitous, II: (100s km, many hours scales) Composite 10x10 deg 3-hourly evolution of IR, PW, 10m divergence around 1st appearance of cold clouds (< 210K) on 0.5 deg grid IR +9h -12h t=0 PW div Meso scale & lifetime clear even in this equal-weight composite (strong rotation cases excluded) Mapes Milliff Morzel in prep.

10. A time domain view in tropical shipborne Doppler radar data lag regression of vertical mass flux w.r.t. surface rain 200 400 p (mb) 600 800 1000 Mapes and Lin 2005 MWR

11. Generic view of organized convection shallow -> deep -> stratiform humid, cloudy heated updrafts dry, rainy downdraft cold conv. outflow shallow cu deep cb stratiform rain anvil ---(hours, days, even weeks)---->

12. So What? asks a Climo-Globo-Dynamician • Mean state implications • Spatial patterns of convxn (tropical biases) • Transient variability • diurnal timing over land • PDF and nonlinear impacts • like ground hydrology • MJO and other tropical waves • apparently impacts ENSO

13. Issues in GCM precipitationDai 2006: ”Precipitation Characteristics in Eighteen Coupled Climate Models” • ...unrealistic double-ITCZ pattern over the tropical Pacific • ...models fail to capture ... large intraseasonal variations • ...too much convective (over 95% of total) and too little stratiform precipitation* • ...underestimate the contribution and frequency for heavy (>20 mm day−1) and overestimate them for light (<10 mm day−1) precipitation...rains too frequently... • ...Intensity... in storm tracks off eastern coasts... too weak... • ...warm-season convection starts too early [in the day]... • *is TRMM conv/strat = convection.F90 / stratiform.F90 ?? • focus on the real process issues: profile, location, timing...

14. Might “organization” help? continuation past mean-state stabilization development sensitivities & feedbacks late stage impacts shallow cu deep cb stratiform rain anvil

15. What this talk ISN’T about, 1 • We are NOT attempting to parameterize the impacts of mesoscale anvils (stratiform precipitation) as another category of moist vertical eddy • a la Donner (1993, 2001) • {other authors literally compare TRMM “stratiform” rain to model “large-scale” rain, presuming that this is the job of the cloud scheme, NOT convection scheme} • philosophical debate needed? elsewhere.

16. What this talk ISN’T about, 2 • We are NOT attempting to parameterize the impacts of two-dimensionality or steadiness (“organization” as used in some contexts), e.g. on vertical momentum flux

17. What this talk IS about • Organization here means sub-grid variations of (mainly) thermodynamic variables, correlated* with convective updraft occurrence “organized” by precipitation “random” *this makes it sound too unlikely -- convection is a highly systematically self-selection process for the most buoyant parcels - “special” parcels are inevitable, common, essential even if the weather doesn’t look “organized” (2D, etc.)

18. Considering convection-LS interactionin param’z’n framework sensitivities of convection (? unknown ?) local column impacts Q1 (T tend.) Q1 aero sol z other e.g. “dyn”? shear upper trop. q T upper Q2 (q tend.) inversions z lower trop. q T in lower trop. PBLmean qe PBL subgrid var. Q3 (u tend.) z (SST) SC DC ST

19. Sensitivity problems (CCM3-CAM2,3)deep convection closed on undilute parcel CAPE sensitivities of convection (? unknown ?) sensitivities of CAPE aero sol other e.g. “dyn”? shear deep layer mean T upper trop. q deep T q aloft inversions lower trop. q lower trop T PBLmean qe PBLmean qe PBL subgrid var. (SST) (SST) Consequences rediscovered repeatedly over past several years in CCSM comm...

20. negative negative Sensitivity determines local feedbacks impacts sensitivities Q1 Q1 z deep layer mean T Q2 q aloft z PBLmean qe Q3 z SC DC ST

21. POSITIVE negative Rain & downdraft feedbacks: not all negative! impacts sensitivities Q1 Q1 aero sol z other shear upper trop. q deep T Q2 inversions z lower trop. q lower trop. T PBL mean qe PBL subgrid var. Q3 z SC DC ST

22. (Updrafts don’t sample a homogeneous mean state every 20 minutes with downdraft outflows blended into PBL)

23. Q3 Sensitivity update (CAM3.5)deep convection closed on dilute parcel CAPE sensitivities of dCAPE sensitivities of CAPE deep layer mean T deep layer mean T q aloft q aloft PBLmean qe PBLmean qe Better variability, incl. ENSO improvement (Neale et al. in prep.) Also impacts update - Richter

24. Sensitivity update (CAM3.5)deep convection closed on dilute parcel CAPE sensitivities of dCAPE • latent heat of freezing also added, to compensate loss of mean CAPE • convection now clusters more in moister parts of space-time (increasing variability) • still, negative feedbacks locally (deep conv. chills PBL, dries, heats) deep layer mean T q aloft PBLmean qe

25. org (tied to recent (3h) rain ordinary PBL T’ (Ok-ARM) What we did for summer vacation • Add “org” as local postive feedbacks params chosen so org ~1 for rainrate of P = 3mm/d Convective closure entrains moister air: (capped at saturation) Didn’t do enough to make its impact clear: try a bigger hammer! Perturb initial parcel T:

26. Compensating for downdraft chilling... • mean heating from ZM convection scheme in single column test • DASH: tests doubling alfa, a downdraft mass flux valve (to its documentation value, then 2x) • DOT: doubling Ke, a “stratiform” rain evaporation parameter, then 4x several K/d in month mean

27. Single column model tests • When it rains, it pours... (increased variance)

28. diurnal delay • Diurnal delay as expected (tau = 3h) • at least in SCM

29. What does it do? Mean state control • Mean state can be more stable (warmer aloft) since convection is happening in org-enhanced areas diff

30. Mean state • Warmer tropics, higher Z300 in tropics,...

31. Mean • ...westerly jet stream changes

32. Drier too Mean • Drier, since deep convection is occurring in special org-enhanced places and buffered from entrainment

33. Mean • Drier, and less cloudy - • except in stratus regions (due to enhanced stability?)

34. Variability • When it rains, it pours

35. Variability • Where it rains, it pours (& the converse)

36. Hotter in the desert (2m T) Focus on Asian monsoon

37. Time variations • 10 days in July • Where it rains it pours • (“noise?”)

38. PDF viewpoint reference CAM with ORG Rather extreme, but maybe a step in a useful direction?

39. Improvements required • org should be able to move • advect w/ low level wind? spread, upshear enhancement, etc.? • (more than we could do here, in parallel code...no neighbors!) • other sources besides precip • precip evaporation, really • subgrid geography? • deformation (gradient tightening)? • impacts on convection deciders should be calibrated • e.g. downdrafts don’t really heat inflow; they just don’t cool it • Ideally, seek consistency w/ PBL and cloud scheme subgrid assumptions - but dist. tails are key to convxn • Resolution dependent (to make whole system more resolution independent)?

40. Beyond CAM-lineage constraints • I wish it governed shallow-deep transition, not just the strength of deep convection • Park-Bretherton unified PBL-convx suite? • We still need to get late-stage impacts right • top-heavy Q1 profile, i.e. impacts observed during stratiform precipitation • “meso” subroutine in convect.f ? water passed to stratiform.f ? should we care ? • really governed by bottom-heavy Q1 elsewhere? • (rain in cu)

41. Too heuristic? • Literalists will want org to be a quantity that can be objectively measured (e.g. in CRMs), not just tuned for impact. • Internal inconsistencies with other subgrid schemes pinch over time • but subgrid dist. & overlap assumptions devised for area (radiation) may not be good for the small but important (systematically self-selecting) buoyant “tail” parcels, especially a key subset (deciders) driving development...

42. Summary • ORG variable used to enhance local positive feedbacks (opposing some excessive local negative feedbacks) • Effect: when it rains, it pours • implication: rest of atm more stable • Bigger dynamic range, delayed diurnal development, more variability, rain persists past marginal stability (discharge-recharge), strong dev. sensitivity to moisture yet without mean-state unstable (cold) biases • some of these seem potentially desirable

44. Convection: a 2-scale circulation Gravity wave speed c time Time mean Cloud C Deformation radius R

45. Convection in a low-res grid time G < G/2c Grid Scale C<G<R c c G/c 2G/c 3G/c C Time mean R

46. Convection’s tendencies in model process & scalecategories convection

47. Example: evaporation and downdrafts • mean heating from ZM convection scheme in single column test • DASH: tests doubling alfa, a downdraft mass flux valve (to its documentation value, then 2x) • DOT: doubling Ke, a “stratiform” rain evaporation parameter, then 4x

48. Arakawa & Schubert(1974): an ensemble of plumes with different entrainment rates li Low-l and high-l clouds compete for PBL moisture Competition decided on Al: integral of b over height of b>0 layer Lowest-l cloud very deep & buoyant: dominates Separate shallow scheme may have to be used (or can tune by specifying critical work function for each cloud type) Effects of minimuml in ensemble

49. Parameterization priorities, 5-10yPlain old Q1 and Q2 are still big Q’s e.g. model MJO Q1 profiles MJO heating (anomaly) profile Lin et al. 2004

50. Pointscale (5’ vertically pointing cloud radar vs. gauge rain, EPIC 2001): anvil dyn. & micro- physics… cu dyn. (multi- cellular) …including stratiform rain