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

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What is organization? Deviations from random parcel/ uniform environment/ no history assumptions embodied in a GCMs convection treatment.

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

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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

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A parsimonious, corrective approach Correction = Expectation[ reality model ] 1.depends on model not just out there to be measured in sky or CRMs 2.depends on field realities of convection not a fiction, not derivable as theory

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

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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.

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New model branch: CAM5_UWens_org 1.Disabled Zhang-McFarlane UW (Bretherton-Park) shallow plume scheme only deep convection too dilute, but a functioning climate 2.I extended code to ensemble of UW plumes unified physical basis for PBL shallow deep TKE / CIN closure buoyancy driven plume fluxes 3.ORG governs plume ensemble members now to demonstrate its worth its weight

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

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b) implementations tested so far wider 2 nd plume plume overlap more frequent rain evap. rain evap. 2 nd plume closure plume base T convection + precipitation org evap2org org2Tpert org2cbmf2 org2rkm2 (appendix) CAM5 with UWens 2- plume ensemble

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

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Only enough time for one result: Org escapes entrainment dilemma An old tradeoff of GCM errors more mixing dilutes updraft buoyancy unstable (e.g. cold aloft) climate biases permitting undilute plumes too unconditional convection, too little variability there is no just right only tradeoffs & compromises not a Goldilocks problem as formulated

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

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

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

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Entrainment dilemma: tropical sounding

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

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

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

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

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Others have roughly same idea A Systematic Relationship between Intraseasonal Variability and Mean State Bias in AGCM Simulations Daehyun Kim, Adam H. Sobel, Eric D. Maloney, Dargan M. W. Frierson, and In-Sik Kang

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

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

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Summary 1.Organization is a set of subgrid variances and relationships that are lacking in average plume/ uniform environment schemes. 2.Entrainment limits convective development, in unorganized cloud fields. 3.Org scheme allows less-dilute convection, once organized. This avoids mean bias from 2. 4.CAM5-UWens-org models exist, they run, and they appear to escape the Entrainment Dilemma. 5.Diurnal cycle delay by orgs timescale (~3h) is a virtue in itself. 6.Further characterization is underway.

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