Background:%20precipitation%20moist%20convection%20

1
The transition to strong convection global
warming tropical rainfall changes
J. David Neelin1, Ole Peters1,2, Matt Munnich1 ,
Chia Chou4, Chris Holloway1, Katrina Hales1,
Joyce Meyerson1, Hui Su3
1Dept. of Atmospheric Sciences Inst. of
Geophysics and Planetary Physics, U.C.L.A. 2Santa
Fe Institute ( Los Alamos National Lab) 3Jet
Propulsion Laboratory 4Academica 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
July
Background Precipitation climatology
January
Note intense tropical moist convection zones
(intertropical convergence zones)
4
8
16
2
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
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 tc 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 BM86BettsMiller 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 109) 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 Neelin1, Ole Peters1,2, Matt Munnich1 ,
Chia Chou4, Chris Holloway1, Katrina Hales1,
Joyce Meyerson1, Hui Su3
1Dept. of Atmospheric Sciences Inst. of
Geophysics and Planetary Physics, U.C.L.A. 2Santa
Fe Institute ( Los Alamos National Lab) 3Jet
Propulsion Laboratory 4Academica 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. (20th century
run,1979-2000) 5 models per panel observed
from CMAP
9
Precipitation change in global warming simulations
Dec.-Feb., 2070-2099 avg minus 1961-90 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 mechanismFormerly M
(anomalous Gross Moist Stability) mechanism1
Descent region incr. descent Þ less precip.
Center of convergence zone incr. moisture
Þ lower gross moist stability Þ incr.
convergence, precip
Chou Neelin, 2004 Held and Soden 2006
34
ECHAM4 ocean mixed layer 2xCO2 equilib.

• Precip. anom. rel.
• to control
• Moisture anom.
• (1000-900 hPa)
• Moisture anom.
• (900-700 hPa)

--- Clim. Precip. (6 mm/day contour)
Chou, Neelin, Tu Chen (2006, J. Clim.)
35
ECHAM4/OPYC3 2070-2099 IS92a (GHG only)

• Precip. anom. rel.
• to control
• Moisture anom.
• (1000-900 hPa)
• Moisture anom.
• (900-700 hPa)

--- Clim. Precip. (6 mm/day contour)
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
Trend of the 10-model ensemble median
Precipitation change measures at the local level
gt 99 significance (1979-2099)
Neelin, Munnich, Su, Meyerson and Holloway ,
2006, PNAS
38
Inter-model precipitation agreement
Number of models (out of 10) with gt 99
significant dry/wet trend (1979-2099) 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 (2070-2099 minus 1951-1980)
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 models 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-21st century pattern) DPrecipdry
• versus tropical average surface air temperature

2070-2099
2040-2069
2010-2039
?Precipdry (mm/day)
1980-2009
Neelin, Munnich, Su, Meyerson and Holloway ,
2006, PNAS
44
Model agreement on amplitudes of tropical changes
(June-Aug. 2070-2099 minus 1901-60)
Surface air temperature DTas
DPrecipdry (dry region projection)
Sensitivity (ratio to Tas)
DPrecipdry/DTas
DPrecipwet/DTas
Vert avg. troposph. temp. DTtrop/DTas
Moisture difference (inside/outside P4mm/day)
/DTas
(each variable scaled to multi-model mean)
Neelin, Munnich, Su, Meyerson and Holloway ,
2006, PNAS
45
Observed precipitation trend in region of high
intermodel agreement
CMAP satellite data set 1979-2003
Land station data CPC (2.5 degrees,
1950-2002) VASCLIMO (1 deg, 1951-2000) 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)
Observed
Caribbean/ C. American region avg.
precip. Cumulative dist. for 50-yr trends
Observed
Estimate of natural variability of 50 year trends
in model control runs without anthropogenic
forcing
47
Model median June-August precipitation trendas
percent of median climatology per century
48
Inter-model Dry/Wet trend agreement
Number of models (out of 10) with gt 99
significant trend (1979-2099), 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
• Þ take qualitative aspects of regional precip.
  changes seriously
• Need to move beyond its warmer so its moister
  to address moisture-temperature relationships in
  deep convection and the interplay with dynamics
  more quantitatively

51
2. The transition to strong convection global
warming tropical rainfall changes
J. David Neelin1, Ole Peters1,2, Matt Munnich1 ,
Chia Chou4, Chris Holloway1, Katrina Hales1,
Joyce Meyerson1, Hui Su3
1Dept. of Atmospheric Sciences Inst. of
Geophysics and Planetary Physics, U.C.L.A. 2Santa
Fe Institute ( Los Alamos National Lab) 3Jet
Propulsion Laboratory 4Academica 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
  well-known 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 SystemsTRMM
  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 - wc( T))/tc (if
  positive)
• w vertical int. water vapor
• wc convective threshold, dependent on
  temperature T
• tc 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
• tA c -MbF
• Closure tA c -t -1( A
  x) , (A x gt 0)
• i.e. Mb (A
  x)(tF)-1 (for Mb gt 0)
• Stochastic modification x in cloud base mass flux
  Mb modifies decay of CAPE (convective
  available potential energy)
• 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 104 to 107 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(z)a(z-zc)b

• 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-wc)b above
  transition
• exponent b 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 sP(w) should become large at
  critical point.
• For susceptibility c(w,L) L2 sP(w,L),
• expect c (w,L) µ Lg/n 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 sP(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-wc)

• Slope of each line (b) 0.215

shifted for clarity
Eastern Pacific
Western Pacific
Atlantic ocean
Indian ocean
(individual fits to b within 0.02)
60
How well do the curves collapse when rescaled?

• Original (seen above)

61
How well do the curves collapse when rescaled?

• Rescale w and P by factors fp, fw for each region
  i

i
i
62
Collapse of Precip. Precip. variance for
different regions

• Slope of each line (b) 0.215

Variance
Eastern Pacific
Western Pacific
Precip
Atlantic ocean
Indian ocean
Western Pacific
Eastern Pacific
Peters Neelin, 2006
63
Precip variance collapse for different averaging
scales
Rescaled by L2
Rescaled by L0.42
64
TMI column water vapor and PrecipitationWestern
Pacific example
65
TMI column water vapor and PrecipitationAtlantic
example
66
Check pick-up with radar precip data

• TRMM radar data for precipitation
• 4 Regions collapse again with wc 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
Mesoscale convective systems

• Cluster size distributions of contiguous cloud
  pixels in mesoscale meteorology almost
  lognormal (Mapes Houze 1993) since Lopez (1977)

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 gt
  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-tG(s/sx)
69
Preliminary water vapor Precip.
relationtemperature dependence
Average
Standard deviation
July ERA40 reanalysis daily Temperature
Tropospheric vertical average (1000-200mb)
70
Dependence on Tropospheric temperature

• Averages conditioned on vert. avg. temp. T, as
  well as w (T 200-1000mb from ERA40 reanalysis)
• Power law fits above critical wc changes, same ?
• note more data points at 270, 271

71
Dependence on Tropospheric temperature

• Find critical water vapor wc 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 wc

73
Frequency of occurrence. drops above critical
Western Pacific for SST within 1C bin of 30C
Frequency of occurrence (all points)
Precip
Frequency of occurrence Precipitating
74
Extending convective quasi-equilibrium

• Recall Critical water vapor wc 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
Extending QE

• 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

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)
Precip
Frequency of occurrence
Hurricane Katrina
80
TMI Precip. Rate Aug. 28, 2005
TMI Precipitation Rate August 28, 2005
0
10
5 millimeters/hr
land
no data
81
Vertical structure of moisture

• 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)

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
Transition probability to Precipgt0

• 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

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 wc 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
The transition to strong convection

• Background
• 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/OPYC3 2030-2050 IS92a (GHG only)

• Precip. anom. rel.
• to control
• Moisture anom.
• (1000-900 hPa)
• Moisture anom.
• (900-700 hPa)

--- Clim. Precip. (6 mm/day contour)
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
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 1992Cumulus Ensemble
Model (2-D)
Precipitation rates (domain avg) Note large
variations Imposed large-scale forcing (cooling
moistening)
Experiments Q03 512 km domain, no shear Q02 512
km domain, shear Q04 1024 km domain, shear