Exploiting observations to seek robust responses in global precipitation
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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 http://www.met.reading.ac.uk/~sgs02rpa

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Exploiting observations to seek robust responses in global precipitation

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

http://www.met.reading.ac.uk/~sgs02rpa

[email protected]


Introduction

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


Exploiting observations to seek robust responses in global precipitation

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


Exploiting observations to seek robust responses in global precipitation

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 (%)


Exploiting observations to seek robust responses in global precipitation

Can we use observations to confirm robust responses?

Precip. (%)

Allan and Soden (2008) Science


Tropical ocean precipitation

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.


Exploiting observations to seek robust responses in global precipitation

Physical basis: energy balance

Trenberth et al. (2009) BAMS


Exploiting observations to seek robust responses in global precipitation

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

Radiative cooling, clear(Wm-2)

Latent Heat Release, LΔP (Wm-2)

Lambert & Webb (2008) GRL

Surface Temperature (K)


Exploiting observations to seek robust responses in global precipitation

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

Allan (2009) J. Clim

Radiative cooling, clear (Wm-2K-1)

NCAS-Climate Talk

15th January 2010


Trends in clear sky radiation in coupled models

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

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 and water vapour

  • Surface net longwave strongly dependent on column water vapour

  • Increased water vapour enhances ability of atmosphere to cool to the surface

ERA40 NCEP SRB SSM/I

Allan (2009) J . Climate


Exploiting observations to seek robust responses in global precipitation

Current changes in tropical ocean column water vapour

John et al. (2009)

Water Vapour (mm)

models

Changing observing systems applied to reanalyses cause spurious variability.


Is the mean state important

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


Does low level moisture rise at 7 k

Does low-level moisture rise at 7%/K?

Specific humidity trend correlation (left) and time series (right)

Willett et al. (2008) J Clim

LandOcean

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.


Evaporation

Evaporation

Richter and Xie (2008) JGR

CCWindTs-ToRHo

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)

NCAS-Climate Talk

15th January 2010


Physical basis moisture transport

Physical Basis: Moisture Transport

scaling

Change in Moisture Transport, dF (pg/day)

If the flow field remains relatively constant, the moisture transport scales with low-level moisture.

Model simulation

Held and Soden (2006) J Climate


Projected top and estimated bottom changes in precipitation minus evaporation d p e

Projected (top) and estimated (bottom) changes in Precipitation minus Evaporation d(P-E)

~

Held and Soden (2006) J Climate


Physical basis circulation response

Physical Basis: Circulation response

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


Exploiting observations to seek robust responses in global precipitation

Physical Basis: Circulation response

P~Mq

Models/observations achieve muted precipitation response by reducing strength of Walker circulation. Vecchi and Soden (2006) Nature


Exploiting observations to seek robust responses in global precipitation

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


Exploiting observations to seek robust responses in global precipitation

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

Extreme Precipitation

Physical basis: water vapour

1979-2002


Exploiting observations to seek robust responses in global precipitation

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.


Exploiting observations to seek robust responses in global precipitation

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


Exploiting observations to seek robust responses in global precipitation

Increases in the frequency of the heaviest rainfall with warming: daily data from models and microwave satellite data (SSM/I)

Reduced frequencyIncreased frequency

Allan et al. (2010) Environ. Res. Lett.


Exploiting observations to seek robust responses in global precipitation

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)


Contrasting precipitation response expected

Contrasting precipitation response expected

Heavy rain follows moisture (~7%/K)

Mean Precipitation linked to radiation balance (~3%/K)

Precipitation 

Light Precipitation (-?%/K)

Temperature 

e.g.Held & Soden (2006) J. Clim; Trenberth et al. (2003) BAMS; Allen & Ingram (2002) Nature


Exploiting observations to seek robust responses in global precipitation

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


Exploiting observations to seek robust responses in global precipitation

Detection of zonal trends

Zhang et al. 2007 Nature


Contrasting wet dry precipitation responses

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

TRMM

  • Robust response: wet regions become wetter at the expense of dry regions. Is this an artefact of the reanalyses?


Contrasting precipitation response in wet and dry regions of the tropical circulation

Contrasting precipitation response in wet and dry regions of the tropical circulation

ascent

Observations

Models

Precipitation change (%)

descent

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 remain?

Avoid reanalyses in defining wet/dry regions

Allan et al. (2010) Environ. Res. Lett.


Current trends in wet dry regions of tropical oceans

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

DRY WET

Models

Allan et al. (2010) Environ. Res. Lett.


Binning by regime

Binning by regime

(a) P (mm/day); % area (b) Model–GPCP P (%) (c) 2080-99 – 1980-99 P (%/K); (d) ω

Vertical motion (ω) percentiles

ascent descent

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


Exploiting observations to seek robust responses in global precipitation

Transient responses

Andrews et al. (2009) J Climate


Transient responses

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

Forcing related fast responses

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)

Precipitation response (%/K)

Andrews et al. (2010) GRL

Can NWP help? e.g. Sean Milton/PAGODA


Outstanding issues

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


Conclusions

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


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