Transcript Document

Exploiting observations to seek robust
responses in global precipitation
Richard P. Allan
Department of Meteorology, University of Reading
Thanks to Brian Soden, Viju John, William Ingram, Peter Good, Igor
Zveryaev and Mark Ringer
[email protected]
“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
How should the water cycle
respond to climate change?
Hawkins and Sutton (2010) Clim. Dyn.
See discussion in: Allen & Ingram (2002) Nature; Trenberth et al. (2003) BAMS
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 (%)
Can we use observations to
confirm robust responses?
Allan and Soden (2008) Science
Tropical ocean precipitation
• dP/dSST:
GPCP: 10%/K
AMIP: 3-11 %/K
• dP/dt trend
GPCP: 1%/dec
AMIP: 0.4-0.7%/dec
Allan et al. (2010) Environ. Res. Lett.
Physical basis: energy balance
NCAS-Climate Talk
15th January 2010
Trenberth et al. (2009) BAMS
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)
Radiative cooling, clear (Wm-2)
Latent Heat Release, LΔP (Wm-2)
e.g. Allen and Ingram (2002) Nature, Stephens & Ellis (2008) J. Clim
Lambert & Webb (2008) GRL
Surface Temperature (K)
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)
Radiative cooling, clear (Wm-2K-1)
e.g. Allen and Ingram (2002) Nature, Stephens & Ellis (2008) J. Clim
NCAS-Climate Talk
15th January 2010
Allan (2009) J. Clim
Trends in clear-sky radiation
in coupled models
Surface net clear-sky longwave
Clear-sky shortwave absorption
Allan (2009) J. Clim
Can we observe atmospheric
radiative heating/cooling?
John et al. (2009) GRL
See also discussion in Trenberth and Fasullo (2010) Science
Surface net longwave and water vapour
• Surface net
longwave strongly
dependent on column
water vapour
• Increased water
vapour enhances
ability of atmosphere
to cool to the surface
Allan (2009) J . Climate
Water Vapour (mm)
Current changes in tropical ocean
column water vapour
John et al. (2009)
Changing observing systems applied to reanalyses
cause spurious variability.
Is the mean state important?
Pierce et
al. (2006)
• 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
Does low-level moisture rise at 7%/K?
Specific humidity trend correlation (left) and time series (right)
Willett et al. (2008) J Clim
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.
Richter and Xie (2008) JGR
CC Wind Ts-To RHo
Muted Evaporation changes in models are
explained by small changes in Boundary Layer:
NCAS-Climate Talk
15th January 2010
1) declining wind stress
2) reduced surface temperature lapse rate (Ts-To)
3) increased surface relative humidity (RHo)
Physical Basis:
Moisture Transport
Change in Moisture
Transport, dF (pg/day)
Model simulation
If the flow field remains
relatively constant, the
moisture transport scales
with low-level moisture.
Held and Soden (2006) J Climate
Projected (top) and
estimated (bottom)
changes in
Precipitation minus
Evaporation d(P-E)
Held and Soden (2006) J Climate
Physical Basis:
Circulation response
First argument:
P ~ Mq.
So if P constrained to rise
more slowly than q, this
implies reduced M
Second argument:
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
Physical Basis:
Circulation response
Models/observations achieve muted precipitation response by
reducing strength of Walker circulation. Vecchi and Soden (2006) Nature
Walker circulation index (top) and sea
level pressure anomalies (bottom)
over equatorial Pacific (1948-2007)
• Evidence for recent
increased strength of
tropical Hadley/Walker
circulation since 1979?
– Sohn and Park (2010) JGR
Hadley circulation index over
15oS-30oN band
Extreme Precipitation
Physical basis: water vapour
• 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
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.
Precipitation Extremes
Trends in tropical wet region
precipitation appear robust.
– What about extreme precipitation events?
• 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?
Increases in the frequency of the heaviest rainfall with warming:
daily data from models and microwave satellite data (SSM/I)
Reduced frequency
Increased frequency
Allan et al. (2010) Environ. Res. Lett.
• 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)
Precipitation 
Contrasting precipitation response expected
Temperature 
e.g.Held & Soden (2006) J. Clim; Trenberth et al. (2003) BAMS; Allen & Ingram (2002) Nature
The Rich Get Richer?
Is there a contrasting
precipitation response in
wet and dry regions?
Models ΔP [IPCC 2007 WGI]
Precip trends, 0-30oN
Rainy season: wetter
Dry season: drier
Chou et al. (2007) GRL
Detection of zonal trends
Zhang et al. 2007 Nature
Contrasting wet/dry
precipitation responses
GPCP Ascent Region
Precipitation (mm/day)
John et al. (2009) GRL
• Large uncertainty in
magnitude of change:
satellite datasets and
models & time period
• Robust response: wet regions become wetter at the
expense of dry regions. Is this an artefact of the reanalyses?
Precipitation change (%)
Contrasting precipitation response in wet
and dry regions of the tropical circulation
Sensitivity to reanalysis dataset used to define wet/dry regions
Allan et al. (2010) Environ. Res. Lett.
Avoid reanalyses in
defining wet/dry regions
• Sample grid boxes:
– 30% wettest
– 70% driest
• Do wet/dry trends
Allan et al. (2010) Environ. Res. Lett.
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
• GPCP trends 1988-2008
– Wet: 1.8%/decade
– Dry: -2.6%/decade
– Upper range of model
trend magnitudes
Allan et al. (2010) Environ. Res. Lett.
Binning by regime
(a) P
(mm/day); % area
1980-99 P (%/K); (d) ω
(b) Model–GPCP P (%)
(c) 2080-99 –
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
Transient responses
Andrews et al. (2009) J Climate
Transient responses
• CO2 forcing experiments
• Initial precip response
supressed by CO2 forcing
• Stronger response after
CO2 rampdown
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
HadCM3: Wu et al. (2010) GRL
Forcing related fast responses
Precipitation response (%/K)
• 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:
Andrews et al. (2010) GRL
Can NWP help? e.g. Sean Milton/PAGODA
(Ming et al. 2010 GRL; also
Andrews et al. 2010 GRL)
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
• 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