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Deep convection --- transition and tails

Deep convection --- transition and tails. J. David Neelin 1,2 , Katrina Hales 1 , Ole Peters 1,5 , Ben Lintner 1,2,7 , Baijun Tian 1,4 , Chris Holloway 3 , Rich Neale 10 , Qinbin Li 1 , Li Zhang 1 , Sam Stechmann 6 , Prabir Patra 8 , Mous Chahine 9.

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Deep convection --- transition and tails

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  1. Deep convection --- transition and tails J. David Neelin1,2, Katrina Hales1, Ole Peters1,5, Ben Lintner1,2,7, Baijun Tian1,4, Chris Holloway3, Rich Neale10, Qinbin Li1, Li Zhang1, Sam Stechmann6, Prabir Patra8, Mous Chahine9 1Dept. of Atmospheric Sciences & 2Inst. of Geophysics and Planetary Physics, UCLA 3University of Reading 4Joint Institute for Regional Earth System Science and Engineering, UCLA 5Imperial College, Grantham Inst. 6Dept. Of Mathematics, UCLA 7Dept. of Environmental Sciences, Rutgers 8Frontier Research Center for Global Change, Japan 9Jet Propulsion Laboratory 10National Center for Atmospheric Research • Characterizing transition to deep convection • Long tails in distributions of column tracers

  2. 2. Transition to strong convection • Convective quasi-equilibrium assumptions: Above onset threshold, convection/precip. increase keeps system close to onset Arakawa & Schubert 1974; Betts & Miller 1986; Moorthi & Suarez 1992; Randall & Pan 1993; Zhang & McFarlane 1995; Emanuel 1993; Emanuel et al 1994; Bretherton et al. 2004; … • Pick up a function of buoyancy-related fields – temperature T & moisture (here column integrated moisture w) • Elsewhere: Onset of strong convection conforms to list of properties for continuous phase transition with critical phenomena(Peters & Neelin 2006, Nature Physics); mesoscale implications (Peters, Neelin & Nesbitt 2009, JAS) • Stochastic convective schemes (and old-fashioned schemes too) need to better characterize the transition to deep convection

  3. Transition to strong, deep convection: Background • Precip increases with column water vapor at monthly, daily time scales(e.g., Bretherton et al 2004).What happens at shorter time scales needed for stochastic convective parameterization, and for strong precip/mesoscale events? • Simple e.g. of convective closure (Betts-Miller 1996)shown for vertical integral: • Precip= (w-wc( T) + x)/tc (if positive, zero otherwise) • w vertical integrated column water vapor • wcconvective threshold, dependent on temperatureT • tc time scale of convective adjustment • Stochastic modification x ( Lin &Neelin, 2000)

  4. Precip. dependence on tropospheric temperature & column water vapor from TMI* • Averages conditioned on vert. avg. temp. T, as well as w (T 200-1000mb from ERA40 reanalysis) • Power law fits above critical: P(w)=a(w-wc)b wc changes, same  • [note more data points at 270, 271] E. Pacific ^ *TMI: Tropical Rainfall Measuring Mission Microwave Imager (Hilburn and Wentz 2008), 20N-20S Neelin, Peters & Hales, 2009, JAS

  5. Collapsed statistics for observed precipitation • Precip. mean & variance dependence on w normalized by critical value wc; occurrence probability for precipitating points (for 4 T values); Event size distribution at Nauru Neelin, Peters, Lin, Holloway & Hales, Phil Trans. Roy. Soc. A, 2008

  6. Example from Manna (1991) lattice model (hopping particles—not a model of convection! 20x20 grid shown) • Activity (order parameter) & variance dependence on particle density (tuning parameter) [conserving case] • Occurrence probability (log scale; very Gaussian) & event size distribution [self organizing case] Neelin, Peters, Lin, Holloway & Hales, Phil Trans. Roy. Soc. A, 2008

  7. TMI precipitation and column water vapor spatial correlations

  8. TMI-AMSRE precipitation and column water vapor temporal correlations

  9. Entraining convective available potential energy and precipitation binned by column water vapor, w • buoyancy & precip. pickup at high w • boundary layer and lower free troposph. moisture contribute comparably* • consistent with importance of lower free tropospheric moisture (Austin 1948; Yoneyama and Fujitani 1995; Wei et al. 1998; Raymond et al. 1998; Sherwood 1999; Parsons et al. 2000; Raymond 2000; Tompkins 2001; Redelsperger et al. 2002; Derbyshire et al. 2004; Sobel et al. 2004; Tian et al. 2006) *Brown & Zhang 1997 entrainment; scheme and microphysics affect onset value, though not ordering. Holloway& Neelin, JAS, 2009 Neelin, Peters, Lin, Holloway & Hales, Phil Trans. Roy. Soc. A, 2008

  10. Obs. Freq. of occurrence of w/wc (precipitating pts) Eastern Pacific for various tropospheric temperatures • Peak just below critical pt.Þself-organization toward wc • But exponential tail above critical pt. Þ more large events • with Gaussian core, akin to forced tracer advection- diffusion problems(e.g. Shraiman & Siggia 1994, Pierrehumbert 2000, Bourlioux & Majda 2002) Gaussian core Critical Exponential tail

  11. Passive tracer advection-diffusion---probability density function from simple flow configuration “Vertical” flow (across gradient) const in vertical, sinusoidal in horizontal, Gaussian in time; horizontal flow constant in space, sinusoid in time Pe= Varying Peclet number S. Stechmann following methods of Bourlioux & Majda 2002 Phys. Fluids

  12. Passive tracer advection-diffusion---probability density function from simple flow configuration “Vertical” flow (across gradient) const in vertical, sinusoidal in horizontal, Gaussian in time; horizontal flow constant in space, sinusoid in time Varying autocorrelation-time tjof flow ´ High Peclet number (low diffusivity) Pe=104 Adapted from Bourlioux & Majda 2002 Phys. Fluids

  13. TMI probability density function for observed column water vapor Anomalies relative to monthly mean, tropical oceans 20S-20N ~exponential on high side Gaussian core (fit at half power) Analysis: Baijun Tian

  14. NCEP reanalysis daily column water vapor probability density function • Anomalies relative to 30-day running mean • Asymmetric exponential tails, assoc. with ascent/descent • Low precip.: symmetric exponential tails Analysis: Ben Lintner

  15. Distribution of Column-int. MOPITT CO obs. &GEOS-Chem simulations 20S-20N & subregions 2000-2005 2001-2006 ~exponential tails Analysis: B. Tian, Q. Li, L. Zhang

  16. Distribution of daily CO2 anomalies • AIRS retrievals (Chahine et al 2005, 2008) (Analysis: Ben Lintner) • GEOS-Chem simulations projected on AIRS weighting functions (Analysis: Qinbin Li, Li Zhang)

  17. Summary • These statistics for precipitation and buoyancy related variables at short time scales provide promising means to quantify the transition to tropical deep convection --- collapse of dependences on temperature and water vapor to simple forms is handy; properties known to appear together in much simpler systems--- it should be possible to capture these in stochastic convection schemes • Tracer distributions consistent with simple prototypes; core with stretched exponential tails ubiquitous for various tracers • Corroborating evidence that the forced tracer advection problem, with the leading effect due to maintained vertical gradient, creates the long tails above critical in column water vapor--- TBD: implications for extreme events

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