The transition to strong convection

Transcription

1 The transition to strong convection & global warming tropical rainfall changes J. David Neelin 1, Ole Peters 1,2, Matt Munnich 1, Chia Chou 4, Chris Holloway 1, Katrina Hales 1, Joyce Meyerson 1, Hui Su 3 1 Dept. of Atmospheric Sciences & Inst. of Geophysics and Planetary Physics, U.C.L.A. 2 Santa Fe Institute (& Los Alamos National Lab) 3 Jet Propulsion Laboratory; 4 Academica Sinica, Taiwan Background: precipitation moist convection & its parameterization Tropical rainfall under global warming The onset of strong convection regime as a continuous phase transition with critical phenomena

2 Background: Precipitation climatology January Note intense tropical moist convection zones (intertropical convergence zones) July mm/day

3 Rainfall at shorter time scales Weekly accumulation Rain rate from a 3-hourly period within the week shown above (mm/hr) From TRMM-based merged data (3B42RT)

4 Background: Convective Quasi-equilibrium closures Manabe et al 1965; Arakawa & Schubert 1974 Arakawa & Schubert 1974; Moorthi & Suarez 1992; Randall & Pan 1993; Emanuel 1991; Raymond 1997; Slow driving (moisture convergence & evaporation, radiative cooling, ) by large scales generates conditional instability Fast removal of buoyancy by moist convective up/down-drafts Above onset threshold, strong convection/precip. increase to keep system close to onset Thus tends to establish statistical equilibrium among buoyancy-related fields temperature T & moisture, including constraining vertical structure using a finite adjustment time scale τ c makes a difference Betts & Miller 1986; Moorthi & Suarez 1992; Randall & Pan 1993; Zhang & McFarlane 1995; Emanuel 1993; Emanuel et al 1994; Yu and Neelin 1994;

5 Convective quasi-equilibrium (Arakawa & Schubert 1974) Modified from Arakawa (1997, 2004) Convection acts to reduce buoyancy (cloud work function A) on fast time scale, vs. slow drive from large-scale forcing (cooling troposphere, warming & moistening boundary layer, ) M65= Manabe et al 1965; BM86=Betts&Miller 1986 parameterizns

6 Departures from QE and stochastic parameterization In practice, ensemble size of deep convective elements in O(200km) 2 grid box x 10minute time increment is not large Expect variance in such an avg about ensemble mean This can drive large-scale variability (even more so in presence of mesoscale organization) Have to resolve convection?! (costs *10 9 ) or stochastic parameterization? [Buizza et al 1999; Lin and Neelin 2000, 2002; Craig and Cohen 2006; Teixeira et al 2007] superparameterization? with embedded cloud model (Grabowski et al 2000; Khairoutdinov & Randall 2001; Randall et al 2002)

7 The transition to strong convection & 1. Global warming tropical rainfall changes J. David Neelin 1, Ole Peters 1,2, Matt Munnich 1, Chia Chou 4, Chris Holloway 1, Katrina Hales 1, Joyce Meyerson 1, Hui Su 3 1 Dept. of Atmospheric Sciences & Inst. of Geophysics and Planetary Physics, U.C.L.A. 2 Santa Fe Institute (& Los Alamos National Lab) 3 Jet Propulsion Laboratory; 4 Academica Sinica, Taiwan Background: precipitation moist convection & its parameterization Tropical rainfall under global warming The onset of strong convection regime as a continuous phase transition with critical phenomena

8 Climatological precip: : Observed vs. 10 coupled models (4 mm/day contour) June - August precipitation climatology Coupled simulation clim. (20 th century run, ); 5 models per panel; observed from CMAP

9 Precipitation change in global warming simulations Dec.-Feb., avg minus avg. 4 mm/day model climatology black contour for reference mm/day Fourth Assessment Report models: LLNL Prog. on Model Diagnostics & Intercomparison; SRES A2 scenario (heterogeneous world, growing population, ) for greenhouse gases, aerosol forcing Neelin, Munnich, Su, Meyerson and Holloway, 2006, PNAS

10 GFDL_CM2.0 DJF Prec. Anom.

11 CCCMA DJF Prec. Anom.

12 CNRM_CM3 DJF Prec. Anom.

13 CSIRO_MK3 DJF Prec. Anom.

14 NCAR_CCSM3 DJF Prec. Anom.

15 GFDL_CM2.1 DJF Prec. Anom.

16 UKMO_HadCM3 DJF Prec. Anom.

17 MIROC_3.2 DJF Prec. Anom.

18 MRI_CGCM2 DJF Prec. Anom.

19 NCAR_PCM1 DJF Prec. Anom.

20 MPI_ECHAM5 DJF Prec. Anom.

21 GFDL_CM2.0 JJA Prec. Anom.

22 CCCMA JJA Prec. Anom.

23 CNRM_CM3 JJA Prec. Anom.

24 CSIRO_MK3 JJA Prec. Anom.

25 NCAR_CCSM3 JJA Prec. Anom.

26 GFDL_CM2.1 JJA Prec. Anom.

27 UKMO_HadCM3 JJA Prec. Anom.

28 MIROC_3.2 JJA Prec. Anom.

29 MRI_CGCM2 JJA Prec. Anom.

30 NCAR_PCM1 JJA Prec. Anom.

31 MPI_ECHAM5 JJA Prec. Anom.

32 The upped-ante mechanism1 Margin of convective zone Neelin, Chou & Su, 2003 GRL

33 The Rich-get-richer mechanism Formerly M (anomalous Gross Moist Stability) mechanism1 Center of convergence zone: incr. moisture lower gross moist stability incr. convergence, precip Descent region: incr. descent less precip. Chou & Neelin, 2004; Held and Soden 2006

34 ECHAM4 + ocean mixed layer 2xCO 2 equilib. Precip. anom. rel. to control --- Clim. Precip. (6 mm/day contour) Moisture anom. ( hpa) Moisture anom. ( hpa) Chou, Neelin, Tu & Chen (2006, J. Clim.)

35 ECHAM4/OPYC IS92a (GHG only) Precip. anom. rel. to control --- Clim. Precip. (6 mm/day contour) Moisture anom. ( hpa) Moisture anom. ( hpa) Chou et al. (2006, J. Clim.)

36 ECHAM4 DJF Contributions to the moisture/mse budget Assoc. with upped ante Assoc. with rich-get-richer (M') mechanism Convergence feedback on both Chou et al, 2006, J. Clim.

37 Precipitation change: measures at the local level Trend of the 10-model ensemble median > 99% significance ( ) Neelin, Munnich, Su, Meyerson and Holloway, 2006, PNAS

38 Inter-model precipitation agreement Number of models (out of 10) with > 99% significant* dry/wet trend ( ) and exceeding 20% of the median clim./century *[Spearman-rho test] Neelin, Munnich, Su, Meyerson and Holloway, 2006, PNAS

39 Global warming (SRES-A2) dry regions: negative precip change ( minus ) overlaid for 6 models (0.5, 2 mm/day contours)

40 Hypothesis for analysis method: models have similar processes for precip increases and decreases but the geographic location is sensitive to differences in model clim. of wind, precip; to variations in the moistening process (shallow convection, moisture closure, )

41 Hypothesis for analysis method: models have similar processes for precip increases and decreases but the geographic location is sensitive Check agreement on amplitude measure: Spatial projection of precip change for each model on that model s own characteristic pattern of change

42 Projection of JJA (30yr running mean) precip pattern onto normalized positive & negative late-century pattern for each model Neelin, Munnich, Su, Meyerson and Holloway, 2006, PNAS

43 Regional precip.. anomaly relation to temperature Dry region precip. anomaly projection (on late-21 st century pattern) ΔPrecip dry versus tropical average surface air temperature ΔPrecip dry (mm/day) Neelin, Munnich, Su, Meyerson and Holloway, 2006, PNAS

44 Model agreement on amplitudes of tropical changes (June-Aug minus ) Surface air temperature ΔT as ΔPrecip dry (dry region projection) Sensitivity (ratio to T as ΔPrecip dry Vert avg. troposph.. temp. ΔT trop /ΔT as Neelin, Munnich, Su, Meyerson and Holloway, 2006, PNAS ): as ): dry /ΔT as ΔPrecip wet /ΔT as Moisture difference (inside/outside P=4mm/day) /ΔT as as (each variable scaled to multi-model mean)

45 Observed precipitation trend in region of high intermodel agreement CMAP satellite data set Land station data: CPC (2.5 degrees, ) VASCLIMO (1 deg, ) Shaded over 95% significance Neelin, Munnich, Su, Meyerson and Holloway, 2006, PNAS

46 50-year trend: obs. drying vs. model control runs Histogram of occurrences of 50-yr. trends (multi-model) Caribbean/ C. American region avg. precip. Observed Observed Cumulative dist. for 50-yr trends Estimate of natural variability of 50 year trends in model control runs without anthropogenic forcing

47 Model median June-August precipitation trend as percent of median climatology per century

48 Inter-model Dry/Wet trend agreement Inter-model Dry/Wet trend agreement Number of models (out of 10) with > 99% significant trend ( ), exceeding 20% of the median clim./century

49 Summary: mechanisms tropospheric warming increases moisture gradient between convective and non-convective regions the "upped-ante mechanism": negative precipitation anomaly regions along margins of convection zones with wind inflow from dry zones the rich-get-richer mechanism" (a.k.a. M ' mechanism): Positive/negative precipitation changes in regions of with high/low climatological precipitation [+ocean heat transport anomaly in equatorial Pacific]

50 Summary: multi-model tropical precipitation change agreement on amplitude of wet/dry precip anoms, despite differing spatial patterns growth with warming for projected precip. patterns; consistency of spatial pattern with time in each model take qualitative aspects of regional precip. changes seriously Need to move beyond it s warmer so it s moister to address moisture-temperature relationships in deep convection and the interplay with dynamics more quantitatively

51 2. The transition to strong convection 2. The transition to strong convection & global warming tropical rainfall changes J. David Neelin 1, Ole Peters 1,2, Matt Munnich 1, Chia Chou 4, Chris Holloway 1, Katrina Hales 1, Joyce Meyerson 1, Hui Su 3 1 Dept. of Atmospheric Sciences & Inst. of Geophysics and Planetary Physics, U.C.L.A. 2 Santa Fe Institute (& Los Alamos National Lab) 3 Jet Propulsion Laboratory; 4 Academica Sinica, Taiwan Background: precipitation moist convection & its parameterization Tropical rainfall under global warming The onset of strong convection regime as a continuous phase transition with critical phenomena

52 2. Transition to strong convection as a continuous phase transition Convective quasi-equilibrium closure postulates (Arakawa & Schubert 1974) of slow drive, fast dissipation sound similar to self-organized criticality (SOC) postulates (Bak et al 1987; ), known in some stat. mech. models to be assoc. with continuous phase transitions (Dickman et al 1998; Sornette 1992; Christensen et al 2004) Critical phenomena at continuous phase transition wellknown in equilibrium case (Privman et al 1991; Yeomans 1992) Data here: Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) precip and water vapor estimates (from Remote Sensing Systems;TRMM radar 2A25 in progress) Analysed in tropics 20N-20S Peters & Neelin, Nature Phys. (2006) + ongoing work.

53 Background Precip increases with column water vapor at monthly, daily time scales (e.g., Bretherton et al 2004). What happens for strong precip/mesoscale events? (needed for stochastic parameterization) E.g. of convective closure (Betts-Miller 1996) shown for vertical integral: Precip = (w w c ( T))/τ c (if positive) w vertical int. water vapor w c convective threshold, dependent on temperature T τ c time scale of convective adjustment

54 Variations about QE: Stochastic convection scheme (CCM3 * & similar in QTCM ** ) Mass flux closure in Zhang - McFarlane (1995) scheme Evolution of CAPE, A, due to large-scale forcing, F t A c = -M b F Closure: t A c = -τ - 1 ( A + ξ), (A + ξ > 0) i.e. M b = (A + ξ)(τf) -1 (for M b > 0) Stochastic modification ξ in cloud base mass flux M b modifies decay of CAPE (convective available potential energy) ξ Gaussian, specified autocorrelation time, e.g. 1 day *Community Climate Model 3 **Quasi-equilibrium Tropical Circulation Model

55 Western Pacific precip vs column water vapor Tropical Rainfall Measuring Mission Microwave Imager (TMI) data Wentz & Spencer (1998) algorithm Average precip P(w) in each 0.3 mm w bin (typically 10 4 to 10 7 counts per bin in 5 yrs) 0.25 degree resolution No explicit time averaging Western Pacific Eastern Pacific Peters & Neelin,, 2006

56 Oslo model (stochastic lattice model motivated by rice pile avalanches) Power law fit: OP(ζ)=a(ζ-ζ c ) β Frette et al (Nature, 1996) Christensen et al (Phys. Res. Lett., 1996; Phys. Rev. E. 2004)

57 Things to expect from continuous phase transition critical phenomena [NB: not suggesting Oslo model applies to moist convection. Just an example of some generic properties common to many systems.] Behavior approaches P(w)= a(w-w c ) β above transition exponent β should be robust in different regions, conditions. ("universality" for given class of model, variable) critical value should depend on other conditions. In this case expect possible impacts from region, tropospheric temperature, boundary layer moist enthalpy (or SST as proxy) factor a also non-universal; re-scaling P and w should collapse curves for different regions below transition, P(w) depends on finite size effects in models where can increase degrees of freedom (L). Here spatial avg over length L increases # of degrees of freedom included in the average.

58 Things to expect (cont.) Precip variance σ P(w) should become large at critical point. For susceptibility χ(w,l)= L 2 σ P(w,L), expect χ (w,l) L γ/ν near the critical region spatial correlation becomes long (power law) near crit. point Here check effects of different spatial averaging. Can one collapse curves for σ P(w) in critical region? correspondence of self-organized criticality in an open (dissipative), slowly driven system, to the absorbing state phase transition of a corresponding (closed, no drive) system. residence time (frequency of occurrence) is maximum just below the phase transition Refs: e.g., Yeomans (1996; Stat. Mech. of Phase transitions, Oxford UP), Vespignani & Zapperi (Phys. Rev. Lett, 1997), Christensen et al (Phys. Rev. E, 2004)

59 log-log Precip. vs (w-w c ) Slope of each line (β) = Eastern Pacific Western Pacific Atlantic ocean Indian ocean shifted for clarity (individual fits to β within ± 0.02)

60 How well do the curves collapse when rescaled? Original (seen above) Western Pacific Eastern Pacific

61 How well do the curves collapse when rescaled? Rescale w and P by i i factors f p, f w for each region i Western Pacific Eastern Pacific

62 Collapse of Precip.. & Precip.. variance for different regions Slope of each line (β) = Variance Eastern Pacific Western Pacific Atlantic ocean Indian ocean Precip Western Pacific Eastern Pacific Peters & Neelin,, 2006

63 Precip variance collapse for different averaging scales Rescaled by L 2 Rescaled by L 0.42

64 TMI column water vapor and Precipitation Western Pacific example

65 TMI column water vapor and Precipitation Atlantic example

66 Check pick-up with radar precip data TRMM radar data for precipitation 4 Regions collapse again with w c scaling Power law fit above critical even has approx same exponent as from TMI microwave rain estimate (2A25 product, averaged to the TMI water vapor grid)

67 Cluster size distributions of contiguous cloud pixels in mesoscale meteorology: almost lognormal (Mapes & Houze 1993) since Lopez (1977) Mesoscale convective systems Mesoscale cluster size frequency (log-normal = straight line). From Mapes & Houze (MWR 1993)

68 Mesoscale cluster sizes from TRMM radar clusters of contiguous pixels with radar signal > threshold (Nesbitt et al 2006) Ranked by size Cluster size distribution alters near critical: increased probability of large clusters Note: spanning clusters not eliminated here; finite size effects in s -τ G(s/s ξ )

69 Preliminary: water vapor Precip.. relation temperature dependence Average Standard deviation July ERA40 reanalysis daily Temperature: Tropospheric vertical average ( mb)

70 Dependence on Tropospheric temperature Averages conditioned on vert. avg. temp. ^ T, as well as w (T mb from ERA40 reanalysis) Power law fits above critical: w c changes, same β [note more data points at 270, 271]

71 Dependence on Tropospheric temperature Find critical water vapor w c for each ^ vert. avg. temp. T (western Pacific) Compare to vert. int. saturation vapor value binned ^ by same T Not a constant fraction of column saturation

72 How much precip occurs near critical point? Contributions to Precip from each T^ 90% of precip in the region occurs above 80% of critical (16% above critical)---even for imperfect estimate of w c 80% of critical critical Water vapor scaled by w c (T) ^

73 Frequency of occurrence.. drops above critical Western Pacific for SST within 1C bin of 30C Frequency of occurrence (all points) Frequency of occurrence Precipitating Precip

74 Extending convective quasi-equilibrium Recall: Critical water vapor w c empirically determined for each vert. avg. ^ temp. T Here use to schematize relationship (& extension of QE) to continuous phase transition/soc properties

75 Extending QE Above critical, large Precip yields moisture sink, (& presumably buoyancy sink) Tends to return system to below critical So frequency of occurrence decreases rapidly above critical

76 Frequency of occurrence max just below critical, contribution to total precip max around & just below critical Strict QE would assume sharp max just above critical, moisture & T pinned to QE, precip det. by forcing Extending QE

77 Extending QE Slow forcing eventually moves system above critical Adjustment: relatively fast but with a spectrum of event sizes, power law spatial correlations, (mesoscale) critical clusters, no single adjustment time

78 Implications Transition to strong precipitation in TRMM observations conforms to a number of properties of a continuous phase transition; + evidence of self-organized criticality convective quasi-equilibrium (QE) assoc with the critical point (& most rain occurs near or above critical) but different properties of pathway to critical point than used in convective parameterizations (e.g. not exponential decay; distribution of precip events, high variance at critical, ) probing critical point dependence on water vapor, temperature: suggests nontrivial relationship (e.g. not saturation curve) spatial scale-free range in the mesoscale assoc with QE Suggests mesoscale convective systems like critical clusters in other systems; importance of excitatory short-range interactions; connection to mesocale cluster size distribution TBD: steps from the new observed properties to better representations in climate models + the temptation of even more severe regimes

79 Precip pick-up & freqency of occurrence relations on a smaller ensemble Aug. 26 to 29, 2005, over the Gulf of Mexico (100W-80W) Frequency of occurrence Precip Hurricane Katrina

80 TMI Precip.. Rate Aug. 28, 2005 TMI Precipitation Rate: August 28, millimeters/hr land no data

81 Ensemble averages of moisture from rawinsonde data at Nauru*, binned by precipitation High precip assoc. with high moisture in free troposphere (consistent with Parsons et al 2000; Bretherton et al 2004; Derbyshire 2005) Vertical structure of moisture *Equatorial West Pacific ARM (Atmospheric Radiation Measurement) project site

82 Autocorrelations in time Long autocorrelation times for vertically integrated moisture (once lofted, it floats around) Nauru ARM site upward looking radiometer + optical gauge Column water vapor Cloud liquid water Precipitation

83 Given column water vapor w at a non-precipitating time, what is probability it will start to rain (here in next hour) Nauru ARM site upward looking radiometer + optical gauge Transition probability to Precip>0

84 Mapping water vapor to occupation probability For geometric questions, consider probability p of site precipating 2D percolation is simplest prototype process (site filled with probability p, stats on clusters of contiguous points); view as null model p incr near critical water vapor w c est from precip power law

85 Mean cluster size increase below critical Check how mean cluster size changes with probability p of precipitating Try against exponent and critical p for site percolation ~ consistent with this null model in a small range below critical; but differs above (to be continued )

86 Background The transition to strong convection QE and Vertical structures Temperature Moisture Continuous phase transition to strong convection Nature Physics Radar & Clusters Critical moisture as a function of temperature

87 Processes competing in (or with) QE Links tropospheric T to ABL, moisture, surface fluxes --- although separation of time scales imperfect Convection + wave dynamics constrain T profile (incl. cold top)

88 ECHAM4/OPYC IS92a (GHG only) Precip. anom. rel. to control --- Clim. Precip. (6 mm/day contour) Moisture anom. ( hpa) Moisture anom. ( hpa) Chou, Neelin, Tu & Chen (2006, J. Clim.)

89 Tropical surface warming (10 models) Tropical avg. (23S-23N) surface air temperature For June-Aug. (30 yr avgs.) SRES A2 scenario forcings

90 Model names Model names cccma_cgcm3.1, Canadian Community Climate Model cnrm_cm3, Meteo-France, Centre National de Recherches Meteorologiques, CM3 Model csiro_mk3.0, CSIRO Atmospheric Research, Australia, Mk3.0 Model gfdl_cm2.0, NOAA Geophysical Fluid Dynamics Laboratory, CM2.0 Model gfdl_cm2.1, NOAA Geophysical Fluid Dynamics Laboratory, CM2.1 Model giss_model_er, NASA Goddard Institute for Space Studies, ModelE20/Russell miroc3.2_medres, CCSR/NIES/FRCGC, MIROC Model V3.2, medium resolution mpi_echam5, Max Planck Institute for Meteorology, Germany, ECHAM5 / MPI OM mri_cgcm2.3.2a, Meteorological Research Institute, Japan, CGCM2.3.2a ncar_ccsm3.0, NCAR Community Climate System Model, CCSM 3.0 ncar_pcm1, Parallel Climate Model (Version 1) ukmo_hadcm3, Hadley Centre for Climate Prediction, Met Office, UK, HadCM3 Model

91 Xu,, Arakawa and Krueger 1992 Cumulus Ensemble Model (2-D) Precipitation rates (domain avg): Note large variations Imposed large-scale forcing (cooling & moistening) Experiments: Q km domain, no shear Q km domain, shear Q km domain, shear