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Parameterizing convective organization

Parameterizing convective organization. Brian Mapes , University of Miami Richard Neale, NCAR. What is organization?. Deviations from random parcel/ uniform environment/ no history assumptions embodied in a GCM’s convection treatment. Worth parameterizing ?.

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Parameterizing convective organization

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  1. Parameterizing convective organization Brian Mapes, University of Miami Richard Neale, NCAR

  2. What is organization? • Deviations from random parcel/ uniform environment/ no history assumptions embodied in a GCM’s convection treatment.

  3. Worth parameterizing? • ...to the degree that errors attributable to those assumptions can be reduced.

  4. A parsimonious, corrective approach • Address the biggest possible bundle (‘EOF1’) of the many phenomena that are lacking, at minimum cost/complexity (1 variable, linear) • Simplicity also commensurate with lack of globally systematic knowledge to base on

  5. A parsimonious, correctiveapproach • Correction = Expectation[ reality – model ] • depends on model • not just “out there” to be measured in sky or CRMs • depends on field realities of convection • not a fiction, not derivable as theory

  6. Example: organization increases during diurnal convective rain development Khairoutdinov and Randall 2006

  7. What increases? • Variance or magnitude of fluctuations, of many variables, at many altitudes • Coherence among above • Scale of fluctuations (slope of size spectrum) • Local environment of coherent structures 4 new variables? No. One.

  8. New model branch: CAM5_UWens_org • Disabled Zhang-McFarlane • UW (Bretherton-Park) ”shallow” plume scheme only • deep convection too dilute, but a functioning climate • I extended code to ensemble of UW plumes • unified physical basis for PBL – shallow – deep • TKE / CIN closure  buoyancy driven plume fluxes • ORG governs plume ensemble members • now to demonstrate it’s worth its weight

  9. a) full proposed organization scheme precipitation evaporation of rain plume overlap more likely (preconditioned local environs) forced, decaying, advected org(lat,lon,t) more, deeper convection subgridgeography and breezes wider plumes with less lateral mixing inhibition/closure stochastic component shear rolls, deformation filaments updraft base T > grid cell mean

  10. b) implementations tested so far precipitation rain evap. plume overlap more frequent (appendix) evap2org org convection + org2rkm2 org2cbmf2 wider 2nd plume CAM5 with UWens 2-plume ensemble 2nd plume closure org2Tpert plume base T’

  11. + Org scheme in CAM5_UWens_org - summer 2010 precipitation evaporation of rain plume overlap more likely (preconditioned local environs) forced, decaying, advected org(lat,lon,t) evap2org =2 tau = 10ks more, deeper convection subgridgeography and breezes wider plumes (entrain less) org2rkm =5 inhibition stochastic component shear (rolls, deformation lines, etc.) org2Tpert = 1 updraft base warmer than grid mean

  12. The Entrainment Dilemma: a well-trod track too undilute (ZM) (CCM3/CAM3) obs. unstable mean state stable dilemma axis: (ZM-Hack-LScond trade-offs) too diluted (CCM2/ Hack, UW shallow only) precip variability 

  13. Entrainment dilemma: tropical sounding UWens with an undilute member: too stable UW only: too diluteunstable state

  14. Dilemma: a well-trod track too undilute (ZM) (CCM3/CAM3) obs. dilution +freezing CAM3.5+ unstable mean state stable dilemma axis: (ZM-Hack-LScond trade-offs) too diluted (CCM2/ Hack, UW shallow only) precip variability 

  15. Entrainment dilemma: tropical sounding (CAM5: UW+ ZM_dil_freez schemes)

  16. Entrainment dilemma: tropical sounding (CAM5: UW+ ZM_dil_freez schemes) UWens with an undilute member: too stable UW only: too diluteunstable state

  17. Org and the entrainment dilemma UWonly: unstable bias, excess variance UW_ens_org: about right

  18. Org and the entrainment dilemma UWonly: unstable bias, excess variance UW_ens_org: about right

  19. Dilemma: a well-trod track IDEA: Org-dependent convection can be restrained by mixing in non-rainy places (increasing variance), while deep convection is less dilute once organized in rainy places (no unstable bias) too undilute (ZM) (CCM3/CAM3) obs. unstable mean state stable dilemma axis: (ZM-Hack-LScond trade-offs) too diluted (CCM2/ Hack, UW shallow only) precip variability 

  20. Others have roughly same idea • “A Systematic Relationship between Intraseasonal Variability and Mean State Bias in AGCMSimulations” • Daehyun Kim, Adam H. Sobel, Eric D. Maloney, DarganM. W. Frierson, and In-SikKang

  21. Hysteresis involving org? dawn stabilization, rain decreases, so org begins to decrease low org STABILITY high org convection persists beginning of rain drives org increase afternoon rain peak NOON DEEP CONVECTION 

  22. ? Hysteresis on longer time scales from org timescale of ~3h ? stabilization, rain decreases, so org begins to decrease low org STABILITY high org convection persists beginning of rain drives org increase DEEP CONVECTION 

  23. Summary • Organization is a set of subgrid variances and relationships that are lacking in average plume/ uniform environment schemes. • Entrainment limits convective development, in unorganized cloud fields. • Org scheme allows less-dilute convection, once organized. This avoids mean bias from 2. • CAM5-UWens-org models exist, they run, and they appear to escape the Entrainment Dilemma. • Diurnal cycle delay by org’s timescale (~3h) is a virtue in itself. • Further characterization is underway.

  24. Help After much delay, hiring postdoc next week for next steps. Unless one of you catches me fast.

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