Presentation on theme: "Exploiting observations to seek robust responses in global precipitation Richard P. Allan Department of Meteorology, University of Reading Thanks to Brian."— Presentation transcript:
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
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
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.
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
Precip. (%) Allan and Soden (2008) Science Can we use observations to confirm robust responses?
NCAS-Climate Talk 15 th January 2010 Trenberth et al. (2009) BAMS Physical basis: energy balance
Surface Temperature (K) Lambert & Webb (2008) GRL Latent Heat Release, LΔP (Wm -2 ) Radiative cooling, clear (Wm -2 ) Models simulate robust response of clear-sky radiation to warming (~2 Wm -2 K -1 ) and a resulting increase in precipitation to balance (~2 %K -1 ) e.g. Allen and Ingram (2002) Nature, Stephens & Ellis (2008) J. Clim
NCAS-Climate Talk 15 th January 2010 Radiative cooling, clear (Wm -2 K -1 ) Allan (2009) J. Clim Models simulate robust response of clear-sky radiation to warming (~2 Wm -2 K -1 ) and a resulting increase in precipitation to balance (~2 %K -1 ) e.g. Allen and Ingram (2002) Nature, Stephens & Ellis (2008) J. Clim
Trends in clear-sky radiation in coupled models Clear-sky shortwave absorptionSurface net clear-sky longwave 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 Allan (2009) J. Climate ERA40 NCEP SRB SSM/I Surface net longwave strongly dependent on column water vapour Increased water vapour enhances ability of atmosphere to cool to the surface
Current changes in tropical ocean column water vapour Changing observing systems applied to reanalyses cause spurious variability. John et al. (2009) models Water Vapour (mm)
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? 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 JGR) which are physically plausible. LandOcean Willett et al. (2008) J Clim
NCAS-Climate Talk 15 th January 2010 CCWindT s -T o RH o Muted Evaporation changes in models are explained by small changes in Boundary Layer: 1) declining wind stress 2) reduced surface temperature lapse rate (T s -T o ) 3) increased surface relative humidity (RH o ) Richter and Xie (2008) JGR Evaporation
Physical Basis: Moisture Transport Change in Moisture Transport, dF (pg/day) If the flow field remains relatively constant, the moisture transport scales with low-level moisture. Model simulation scaling 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 ~
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 (Г d -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
Models/observations achieve muted precipitation response by reducing strength of Walker circulation. Vecchi and Soden (2006) Nature P~Mq Physical Basis: Circulation response
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 ( ) Hadley circulation index over 15 o S-30 o N 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 -OGorman 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: OGorman & 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.
Analyse daily rainfall over tropical oceans –SSM/I v6 satellite data, (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
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.
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 GRL) Links to physical mechanisms/relationships required (Haerter et al GRL)
Contrasting precipitation response expected Precipitation 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
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, 0-30 o N The Rich Get Richer?
Zhang et al Nature Detection of zonal trends
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?
Contrasting precipitation response in wet and dry regions of the tropical circulation Allan et al. (2010) Environ. Res. Lett. descent ascent ModelsObservations Precipitation change (%) Sensitivity to reanalysis dataset used to define 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 Wet/dry trends remain – GPCP record may be suspect for dry region –SSM/I dry region record: inhomogeneity 2000/01? GPCP trends –Wet: 1.8%/decade –Dry: -2.6%/decade –Upper range of model trend magnitudes Models DRY WET Allan et al. (2010) Environ. Res. Lett.
Binning by regime (a) P (mm/day); % area (b) Model–GPCP P (%) (c) – 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 minus e.g. see also Emori and Brown (2005) GRL
Andrews et al. (2009) J Climate Transient responses
CO 2 forcing experiments Initial precip response supressed by CO 2 forcing Stronger response after CO 2 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
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 CO 2 physiological effect potentially substantial (Andrews et al Clim. Dyn.; Dong et al J. Clim) Hydrological Forcing: HF=kdT-dAA-dSH (Ming et al GRL; also Andrews et al GRL) Precipitation response (%/K) Can NWP help? e.g. Sean Milton/PAGODA
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 Conclusions