Transcript 1-A

Current Changes in the
Global Water Cycle
Richard P. Allan
Diffusing slowly to Met Department/NCAS-Climate from ESSC
Thanks to Brian Soden, Viju John, William Ingram, Peter Good,
Igor Zveryaev, Mark Ringer and Tony Slingo
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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?
Precipitation Change (%) relative to 1961-1990: 2 scenarios, multi model (IPCC, 2001)
See discussion in: Allen & Ingram (2002) Nature; Trenberth et al. (2003) BAMS
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Climate model projections (IPCC 2007)
Precipitation Intensity
•
•
•
•
Increased Precipitation
More Intense Rainfall
More droughts
Wet regions get wetter,
dry regions get drier?
• Regional projections??
Dry Days
Precipitation Change (%)
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Physical basis: energy balance
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Trenberth et al. (2009) BAMS
Models simulate robust response of clear-sky
radiation to warming (~2-3 Wm-2K-1) and a resulting
increase in precipitation to balance (~3 %K-1)
Radiative cooling, clear (Wm-2K-1)
e.g. Allen and Ingram (2002) Nature, Stephens & Ellis (2008) J. Clim,
Lambert and Webb (2008) GRL
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Allan (2009) J Clim
Physical basis: water vapour
• Clausius-Clapeyron
–
–
–
–
Low-level water vapour (~7%/K)
Intensification of rainfall
Moisture transport
Enhanced P-E patterns
See Held and Soden (2006) J Clim
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1979-2002
Evaporation
Richter and Xie (2008) JGR
CC Wind Ts-To RHo
Muted Evaporation changes in models are
explained by small changes in Boundary Layer:
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1) declining wind stress
2) reduced surface temperature lapse rate (Ts-To)
3) increased surface relative humidity (RHo)
Current changes in the water cycle
As observed by satellite datasets and
simulated by models
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Water Vapour (mm)
Current changes in tropical ocean
column water vapour
John et al. (2009)
models
AMIP3 CMIP3 CMIP3 volc
Allan (2009)
- see also Trenberth et al. (2005) Clim. Dyn.,
Soden et al. (2005) Science
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ERA40
NCEP
ERAINT
SSM/I
…despite inaccurate mean state, Pierce et al.;
John and Soden (both GRL, 2006)
Tropical ocean precipitation
• dP/dSST:
GPCP: 10%/K
(1988-2008)
AMIP: 3-11 %/K
(1979-2001)
SSM/I
GPCP
• dP/dt trend
GPCP: 1%/dec
(1988-2008)
AMIP: 0.4-0.7%/dec
(1979-2001)
(land+ocean)
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Wet (ascent) and Dry
(descent) regimes
GPCP Ascent Region
Precipitation (mm/day)
• Robust response:
wet regions become
wetter at the expense
of dry regions
TRMM
John et al. (2009) GRL
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• Large uncertainty in
magnitude of change:
satellite datasets and
models & time period
Precipitation change (%)
Contrasting precipitation response in wet
and dry regions of the tropical circulation
ascent
Observations
Models
descent
Sensitivity to reanalysis dataset used to define wet/dry regions
Updated from Allan and Soden (2007) GRL
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Avoid reanalyses in
defining wet/dry
regions
• Sample grid boxes:
– 30% wettest
– 70% driest
• Do wet/dry trends
remain?
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Current trends in wet/dry
regions of tropical oceans
• Wet/dry trends remain
WET
– 1979-1987 GPCP
record may be suspect
for dry region
– SSM/I dry region
record: inhomogeneity
2000/01?
DRY
Models
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• GPCP trends 1988-2008
– Wet: 1.8%/decade
– Dry: -2.6%/decade
– Upper range of model
trend magnitudes
Precipitation Extremes
Trends in tropical wet region
precipitation appear robust.
– What about extreme precipitation events?
METHOD
• Analyse daily rainfall over tropical oceans
– SSM/I satellite data, 1988-2008
– Climate model data (AMIP experiments)
• Create monthly PDFs of rainfall intensity
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• Calculate changes in the frequency of
events in each intensity bin
• Does frequency of most intense rainfall
rise with atmospheric warming?
Increases in the frequency of the heaviest rainfall with warming:
daily data from models and microwave satellite data (SSM/I)
Reduced frequency
Increased frequency
Updated from Allan and Soden (2008) Science
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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
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No apparent link to convection scheme? What about CMIP
experiments? e.g. Turner and Slingo (2009) ASL
One of the largest challenges
remains improving predictability of
regional changes in the water cycle…
Changes in circulation systems are
crucial to regional changes in water
resources and risk yet predictability
is poor.
How will catchment-scale runoff and
crucial local impacts and risk respond to
warming?
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Precipitation in
the EuropeAtlantic region
(summer)
Dependence
on NAO
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Water vapourPrecipitation
Current changes in the water
cycle over Europe-Atlantic region
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Outstanding issues
• Are satellite estimates of precipitation,
evaporation and surface flux variation reliable?
• Are regional changes in the water cycle, down to
catchment scale, predictable?
• How well do models represent land surface
feedbacks. Can SMOS mission help?
• How is the water cycle responding to aerosols?
• Linking water cycle and cloud feedback issues
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Extra Slides
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Conclusions
• Robust Responses
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–
–
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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 PDFs
– Magnitude of change in precipitation in 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
– Surface feedbacks: ocean salinity, soil moisture (SMOS?)
– Boundary layer changes and surface fluxes
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Wet
Dry
dPw/dT=7%/K
Pw=6 mm/day
A=0.4
dPd/dT
Pd=1 mm/day
(1-A)=0.7
P=3 mm/day
dP/dT=3%/K
A is the wet region
fractional area
P is precipitation
T is temperature
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DISCUSSION
Assume wet region follows
Clausius Clapeyron (7%/K)
and mean precip follows
radiation constraint (~3%/K)
Wet
Dry
dPw/dT=7%/K
Pw=6 mm/day
A=0.4
dPd/dT
Assume wet region follows
Clausius Clapeyron (7%/K)
and mean precip follows
radiation constraint (~3%/K)
Pd=1 mm/day
(1-A)=0.7
dP/
A(dPw/dT)+(1-A)(dPd/dT)
 dPd= (dP-AdPw)/(1-A)
P=3 mm/day
dT=
dP/dT=3%/K
A
Pw
Pd
dPd/dTs
(mm/day/K)
“
(%/K)
0.4
0.2
6
9
1
1.5
-0.1
-0.05
-10
-4.5
0.1
10.5 2.2
+0.02
+0.9
A is the wet region
fractional area
P is precipitation
T is temperature
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SMOS
ESA’s SMOS (Soil Moisture and Ocean Salinity) launched
November 2009
Evaporation changes over land are not globally measured. New
data on soil moisture could be vital in understanding changes in
evaporation and regional water cycle feedbacks over land.
The addition of ocean salinity measurements are also of potential
value in understanding P-E changes and ocean circulating
response
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Cloud Feedback
• Can HadIR provide any information on cloud feedback
• For example, the FAT hypothesis (fixed anvil
temperature):
– Anvil outflow determined by position of zero radiative cooling
– …which is determined by the rapid decline in water vapour with
altitude
– …which is determined by Clausius Clapeyron
– Hypothesis: As temperature rises, outflow rises in altitude but not
temperature which remains fixed
– e.g. Hartmann and Larson (2003); Zelinka and Hartmann in
press
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Are the issues of cloud feedback
and the water cycle linked?
2008
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Response of the hydrological cycle
is sensitive to the type of forcing
Andrews et al. (2009) J Climate
Partitioning of energy between atmosphere and
surface is crucial to the hydrological response; this
is being assessed in the PREPARE project
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How does UTH respond to warming?
Minschwaner et al. (2006)
J Clim
Lindzen (1990) BAMS
Mitchell et al. (1987)
QJRMS
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Radiation budget, hydrological
cycle and climate feedbacks
Precipitation projections (IPCC)
Precip.
(%)
Decadal changes in water vapour,
precipitation and its extremes
are beginning to be detected
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Allan and Soden (2008) Science