CHAPTER 6 PRECIPITATION EXTREMES CHAPTER 6 PRECIPITATION EXTREMES.
Exploiting observations to seek robust responses in global precipitation
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Exploiting observations to seek robust responses in global precipitation
Richard P. AllanDepartment of Meteorology, University of Reading
Thanks to Brian Soden, Viju John, William Ingram, Peter Good, Igor Zveryaev and Mark Ringer
http://www.met.reading.ac.uk/~sgs02rpa [email protected]
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Introduction
“Observational records and climate projections provide abundant evidence that freshwater resources are vulnerable and have the potential to be strongly impacted by climate change, with wide-ranging consequences for human societies and ecosystems.” IPCC (2008) Climate Change and Water
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How should the water cycle respond to climate change?
See discussion in: Allen & Ingram (2002) Nature; Trenberth et al. (2003) BAMS
Hawkins and Sutton (2010) Clim. Dyn.
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• Increased Precipitation• More Intense Rainfall• More droughts• Wet regions get wetter, dry
regions get drier?• Regional projections??
Precipitation Change (%)
Climate model projections (IPCC 2007)
Precipitation Intensity
Dry Days
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Pre
cip.
(%
)
Allan and Soden (2008) Science
Can we use observations to confirm robust responses?
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Tropical ocean precipitation
• dP/dSST:
GPCP: 10%/K (1988-2008)
AMIP: 3-11 %/K (1979-2001)
• dP/dt trend
GPCP: 1%/dec(1988-2008)
AMIP: 0.4-0.7%/dec(1979-2001)
(land+ocean)
SSM/I GPCP
Allan et al. (2010) Environ. Res. Lett.
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NCAS-Climate Talk 15th January 2010 Trenberth et al. (2009) BAMS
Physical basis: energy balance
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Surface Temperature (K)
Lambert & Webb (2008) GRL
Late
nt H
eat
Rel
ease
, LΔ
P (
Wm
-2)
Rad
iativ
e co
olin
g, c
lear
(W
m-2)
Models simulate robust response of clear-sky radiation to warming (~2 Wm-2K-1) and a resulting increase in precipitation to balance (~2 %K-1)
e.g. Allen and Ingram (2002) Nature, Stephens & Ellis (2008) J. Clim
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NCAS-Climate Talk 15th January 2010
Rad
iativ
e co
olin
g, c
lear
(W
m-2K
-1)
Allan (2009) J. Clim
Models simulate robust response of clear-sky radiation to warming (~2 Wm-2K-1) and a resulting increase in precipitation to balance (~2 %K-1)
e.g. Allen and Ingram (2002) Nature, Stephens & Ellis (2008) J. Clim
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Trends in clear-sky radiation in coupled models
Clear-sky shortwave absorptionSurface net clear-sky longwave
Allan (2009) J. Clim
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Can we observe atmospheric radiative heating/cooling?
John et al. (2009) GRL
See also discussion in Trenberth and Fasullo (2010) Science
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Surface net longwave and water vapour
Allan (2009) J . Climate
ER
A40
N
CE
P
SR
B
SS
M/I
• Surface net longwave strongly dependent on column water vapour
• Increased water vapour enhances ability of atmosphere to cool to the surface
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Current changes in tropical ocean column water vapour
Changing observing systems applied to reanalyses cause spurious variability.
John et al. (2009)
models
Wat
er V
apou
r (m
m)
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Is the mean state important?
• Models appear to overestimate water vapour– Pierce et al. (2006) GRL;
John and Soden (2006) GRL– But not for microwave data?
[Brogniez and Pierrehumbert (2007) GRL]
• This does not appear to affect feedback strength– John and Soden (2006)
• What about the hydrological cycle?– Symptomatic of inaccurate
simulation?
Pierce et al. (2006)
GRL
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Does low-level moisture rise at 7%/K?Specific humidity trend correlation (left) and time series (right)
Willett et al. (2007) Nature
Robust relationships globally.
Less coherent relationships regionally/over land/at higher altitudes?
Evidence for reductions in RH over land (Simmons et al. 2009 JGR) which are physically plausible.
Land OceanWillett et al. (2008) J Clim
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NCAS-Climate Talk 15th January 2010
CC Wind Ts-To RHo
Muted Evaporation changes in models are explained by small changes in Boundary Layer:1) declining wind stress2) reduced surface temperature lapse rate (Ts-To)3) increased surface relative humidity (RHo)
Richter and Xie (2008) JGR
Evaporation
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Physical Basis: Moisture Transport
Cha
nge
in M
oist
ure
Tra
nspo
rt,
dF (
pg/d
ay)
If the flow field remains relatively constant, the moisture transport scales with low-level moisture.Model simulation
scaling
Held and Soden (2006) J Climate
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Projected (top) and estimated (bottom)
changes in Precipitation minus Evaporation d(P-E)
Held and Soden (2006) J Climate
~
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First argument:P ~ Mq.
So if P constrained to rise more slowly than q, this implies reduced M
Second argument:ω=Q/σ.
Subsidence (ω) induced by radiative cooling (Q) but the magnitude of ω depends on (Гd-Г) or static stability (σ).
If Г follows MALR increased σ. This offsets Q effect on ω.See Held & Soden (2006) and Zelinka & Hartmann (2010) JGR
P~Mq
Physical Basis: Circulation response
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Models/observations achieve muted precipitation response by reducing strength of Walker circulation. Vecchi and Soden (2006) Nature
P~Mq
Physical Basis: Circulation response
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• Evidence for recent increased strength of tropical Hadley/Walker circulation since 1979?– Sohn and Park (2010) JGR
Walker circulation index (top) and sea level pressure anomalies (bottom) over equatorial Pacific (1948-2007)
Hadley circulation index over 15oS-30oN band
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Extreme PrecipitationPhysical basis: water vapour
1979-2002• Clausius-Clapeyron– Low-level water vapour (~7%/K)– Intensification of rainfall: Trenberth et al.
(2003) BAMS; Pall et al. (2007) Clim Dyn
• Changes in intense rainfall also constrained by moist adiabat -O’Gorman and Schneider (2009) PNAS
• Does extra latent heat release within storms enhance rainfall intensity above Clausius Clapeyron?– e.g. Lenderink and van Meijgaard (2010)
Environ. Res. Lett.; Haerter et al. (2010) GRL
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Changes in Extreme Precipitation Determined by changes in low-level water vapour and updraft velocity
Above: O’Gorman & Schneider (2008) J Clim
Aqua planet experiment shows extreme precipitation rises with surface q, a lower rate than column water vapour
Right: Gastineau and Soden (2009) GRL Reduced frequency of upward motion
offsets extreme precipitation increases.
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• Analyse daily rainfall over tropical oceans– SSM/I v6 satellite data, 1988-2008 (F08/11/13)– Climate model data (AMIP experiments)
• Create rainfall frequency distributions
• Calculate changes in the frequency of events in each intensity bin
• Does frequency of most intense rainfall rise with atmospheric warming?
Precipitation Extremes
Trends in tropical wet region precipitation appear robust.
– What about extreme precipitation events?
METHOD
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Increases in the frequency of the heaviest rainfall with warming: daily data from models and microwave satellite data (SSM/I)
Reduced frequency Increased frequencyAllan et al. (2010) Environ. Res. Lett.
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• Increase in intense rainfall with tropical ocean warming (close to Clausius Clapeyron)
• SSM/I satellite observations at upper range of model range
Turner and Slingo (2009) ASL: dependence on convection scheme?
Observational evidence of changes in intensity/duration (Zolina et al. 2010 GRL)
Links to physical mechanisms/relationships required (Haerter et al. 2010 GRL)
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Contrasting precipitation response expected
Pre
cipi
tatio
n Heavy rain follows moisture (~7%/K)
Mean Precipitation linked to
radiation balance (~3%/K)
Light Precipitation (-?%/K)
Temperature e.g.Held & Soden (2006) J. Clim; Trenberth et al. (2003) BAMS; Allen & Ingram (2002) Nature
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Models ΔP [IPCC 2007 WGI]
Is there a contrasting precipitation response in wet and dry regions?
Rainy season: wetter
Dry season: drier Chou et al. (2007) GRL
Precip trends, 0-30oN
The Rich Get Richer?
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Zhang et al. 2007 Nature
Detection of zonal trends
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Contrasting wet/dry precipitation responses
• Large uncertainty in magnitude of change: satellite datasets and models & time period
TRMM
GPCP Ascent Region Precipitation (mm/day)
John et al. (2009) GRL
• Robust response: wet regions become wetter at the expense of dry regions. Is this an artefact of the reanalyses?
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Contrasting precipitation response in wet and dry regions of the tropical circulation
Allan et al. (2010) Environ. Res. Lett.
descent
ascentModelsObservations
Pre
cipi
tatio
n ch
ange
(%
)
Sensitivity to reanalysis dataset used to define wet/dry regions
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• Sample grid boxes:– 30% wettest– 70% driest
• Do wet/dry trends remain?
Avoid reanalyses in defining wet/dry regions
Allan et al. (2010) Environ. Res. Lett.
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Current trends in wet/dry regions of tropical oceans
• Wet/dry trends remain– 1979-1987 GPCP
record may be suspect for dry region
– SSM/I dry region record: inhomogeneity 2000/01?
• GPCP trends 1988-2008
– Wet: 1.8%/decade– Dry: -2.6%/decade– Upper range of model
trend magnitudes
Models
DR
Y
WE
T
Allan et al. (2010) Environ. Res. Lett.
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Binning by regime(a) P (mm/day); % area (b) Model–GPCP P (%) (c) 2080-99 – 1980-99 P (%/K); (d) ω
Vert
ical m
oti
on
(ω
)
perc
en
tile
s
ascen
t
descen
t
Precipitation binned in percentiles of vertical motion (0-5% bin is strongest ascent) and temperature (95-100% bin is warmest) for the HadGEM1 model (top) and an ensemble of 10 CMIP3 models (bottom). (a) Mean precipitation and % area enclosed in each contour, (b) model – GPCP precipitation and model change in (c) precipitation (scaled by temperature change) and (d) vertical motion for 2080-99 minus 1980-99. e.g. see also Emori and Brown (2005) GRL
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Andrews et al. (2009) J Climate
Transient responses
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Transient responses• CO2 forcing experiments
• Initial precip response supressed by CO2 forcing
• Stronger response after CO2 rampdown
HadCM3: Wu et al. (2010) GRL
CMIP3 coupled model ensemble mean: Andrews et al. (2010) Environ. Res. Lett.
Degree of hysteresis determined by forcing related fast responses and linked to ocean heat uptake
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Forcing related fast responses
Andrews et al. (2010) GRL
Total Slow
• Surface/Atmospheric forcing determines “fast” precipitation response
• Robust slow response to T• Mechanisms described in Dong
et al. (2009) J. Clim
• CO2 physiological effect potentially substantial (Andrews et al. 2010 Clim. Dyn.; Dong et al. 2009 J. Clim)
• Hydrological Forcing:
HF=kdT-dAA-dSH
(Ming et al. 2010 GRL; also Andrews et al. 2010 GRL)
Pre
cip
itatio
n re
spo
nse
(%
/K)
Can NWP help? e.g. Sean Milton/PAGODA
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Outstanding issues
• Satellite estimates of precipitation, evaporation and surface flux variation are not reliable.
• Are regional changes in the water cycle, down to catchment scale, predictable?
• How well do models represent land surface and physiological feedbacks.
• How is the water cycle responding to aerosols?• Linking water cycle and cloud feedback issues
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• Robust Responses– Low level moisture; clear-sky radiation
– Mean and Intense rainfall
– Observed precipitation response at upper end of model range?
– Contrasting wet/dry region responses
• Less Robust/Discrepancies– Moisture at upper levels/over land and mean state
– Inaccurate precipitation frequency/intensity distributions
– Magnitude of change in precipitation from satellite datasets/models
• Further work– Decadal changes in global energy budget, aerosol forcing effects
and cloud feedbacks: links to water cycle?
– Precipitation and radiation balance datasets: forward modelling
– Separating forcing-related fast responses from slow SST response
– Boundary layer changes and surface fluxes
Conclusions