Transcript The transition to strong convection & global warming tropical rainfall changes J.

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
Background: Precipitation climatology
January
Note intense tropical
moist convection zones
(intertropical
convergence zones)
July
2 4
8
16
mm/day
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)
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; …
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
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)
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
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
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
GFDL_CM2.0
DJF Prec. Anom.
CCCMA
DJF Prec. Anom.
CNRM_CM3
DJF Prec. Anom.
CSIRO_MK3
DJF Prec. Anom.
NCAR_CCSM3
DJF Prec. Anom.
GFDL_CM2.1
DJF Prec. Anom.
UKMO_HadCM3
DJF Prec. Anom.
MIROC_3.2
DJF Prec. Anom.
MRI_CGCM2
DJF Prec. Anom.
NCAR_PCM1
DJF Prec. Anom.
MPI_ECHAM5
DJF Prec. Anom.
GFDL_CM2.0
JJA Prec. Anom.
CCCMA
JJA Prec. Anom.
CNRM_CM3
JJA Prec. Anom.
CSIRO_MK3
JJA Prec. Anom.
NCAR_CCSM3
JJA Prec. Anom.
GFDL_CM2.1
JJA Prec. Anom.
UKMO_HadCM3
JJA Prec. Anom.
MIROC_3.2
JJA Prec. Anom.
MRI_CGCM2
JJA Prec. Anom.
NCAR_PCM1
JJA Prec. Anom.
MPI_ECHAM5
JJA Prec. Anom.
The “upped-ante” mechanism
1
Margin of
convective zone
Neelin, Chou & Su, 2003 GRL
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
ECHAM4 + ocean mixed layer 2xCO2 equilib.
Precip. anom. rel.
to control
--- Clim. Precip.
(6 mm/day contour)
Moisture anom.
(1000-900 hPa)
Moisture anom.
(900-700 hPa)
Chou, Neelin, Tu & Chen (2006,
J. Clim.)
ECHAM4/OPYC3 2070-2099 IS92a (GHG only)
Precip. anom. rel.
to control
--- Clim. Precip.
(6 mm/day contour)
Moisture anom.
(1000-900 hPa)
Moisture anom.
(900-700 hPa)
Chou et al. (2006, J. Clim.)
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.
Precipitation change: measures at the local level
Trend of the 10-model ensemble median
> 99% significance (1979-2099)
Neelin, Munnich, Su, Meyerson and Holloway , 2006, PNAS
Inter-model precipitation agreement
Number of models (out of 10) with > 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
Global warming (SRES-A2) dry regions:
negative precip change (2070-2099 minus 1951-1980)
overlaid for 6 models (0.5, 2 mm/day contours)
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,…)
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
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
Regional precip. anomaly relation to temperature
DPrecipdry (mm/day)
Dry region precip. anomaly projection
(on late-21st century pattern) DPrecipdry
versus tropical average surface air temperature
2040-2069
2070-2099
2010-2039
1980-2009
Neelin, Munnich, Su, Meyerson and Holloway , 2006, PNAS
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 P=4mm/day) /DTas
Neelin, Munnich, Su, Meyerson and Holloway , 2006, PNAS
(each variable scaled to multi-model mean)
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]
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
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
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 ….
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
Western Pacific precip vs column water vapor
• Tropical Rainfall Measuring
Mission Microwave Imager
(TMI) data
Western Pacific
• Wentz & Spencer (1998)
algorithm
• Average precip P(w) in each
Eastern Pacific
4
0.3 mm w bin (typically 10 to
107 counts per bin in 5 yrs)
• 0.25 degree resolution
• No explicit time averaging
Peters & Neelin, 2006
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)
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.
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)  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)
log-log Precip. vs (w-wc)
• Slope of each line (b) = 0.215
shifted
for
clarity
(individual fits to b within ± 0.02)
How well do the curves collapse when rescaled?
• Original (seen above)
Western Pacific
Eastern Pacific
How well do the curves collapse when rescaled?
• Rescale w and P by
factors fpi, fwi for each
region i
Western Pacific
Eastern Pacific
Collapse of Precip. & Precip. variance for different
regions
• Slope of each line (b) = 0.215
Variance
Precip
Western Pacific
Eastern Pacific
Peters & Neelin, 2006
Precip variance collapse for
different averaging scales
Rescaled by L2
Rescaled by L0.42
TMI column water vapor and Precipitation
Western Pacific example
TMI column water vapor and Precipitation
Atlantic example
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 b
• [note more data
points at 270, 271]
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
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
80%
of critical
critical
^
Water vapor scaled by wc (T)
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
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
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
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
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 …
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 …
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
TMI Precip. Rate Aug. 28, 2005
TMI Precipitation Rate: August 28, 2005
0
5
millimeters/hr
land
no data
10