PREPARE: Projected Responses in Extreme Precipitation and Atmospheric Radiative Energy

1. PREPARE: Projected Responses in Extreme Precipitation and Atmospheric Radiative Energy Richard P. Allan, Matthias Zahn Department of Meteorology, University of Reading Met Office Collaborators: Viju John, Mark Ringer, Alejandro Bodas-Salcedo, Peili Wu International Collaborators: Martin Wild (ETH, Zurich) Brian Soden (RSMAS, University of Miami), Igor Zveryaev (PP Shirshov, Moscow)‏

2. PREPARE project: details • Funded by NERC; collaboration with Met Office and ETH Zurich • 1 March 2010-30 April 2013 • Also: NERC Changing Water Cycle (CWC): • PAGODA CWC project (Reading/Edinburgh/Exeter/Met Office) • Imperial College CWC project (Imperial/Reading) • TAMSAT group (Reading) • Work toward AR5 • Aim of today: to to make each other aware of current work relating to PREPARE and opportunities for collaboration. In particular, how can the project be of use to the Met Office and what resources, for example model experiments/datasets, would the Met Office be able to contribute to the project.

3. How will precipitation change in the future? How is precipitation currently changing? Do we understand projected responses? Wet/Dry region responses Extreme precipitation Model Precipitation Change (%)‏ Allan et al. (2010) Environ. Res. Lett.

4. Current Understanding • Observations and simple physics help to confirm robust model projections • Increased precipitation (~2%/K) • Increased precipitation intensity (~7%/K) • Extratropics and wet regions of tropics get wetter • Dry regions of sub-tropics get drier • Transient response to GHG stabilisation • Outstanding Issues • Inaccurate simulation of precipitation events • Limitations of satellite and gauge data • Cloud Feedback and Aerosol Forcing • is Walker circulation modulated by contrasting effects of SST and forcing (e.g. CO2)? • Detecting and attributing signals

5. Recent Advances • Surface and atmosphere energy balance constraint (Allen and Ingram, 2002; Stephens and Ellis, 2008; Lambert and Webb, 2008, Andrews et al. 2009; Richter and Xie 2008 JGR) • Moisture transport constraint (Held and Soden, 2006) • Dynamical considerations (O’Gorman and Schneider, 2009; Lendering and Van Meijgaard, 2008; Turner and Slingo 2009 ASL) • Mass flux and moist adiabat arguments (e.g. Vecchi and Soden 2006 Nature; Sohn and Park 2010 JGR) • Transient response(Andrews et al. 2010 ERL; Wu et al. 2010 GRL; Bala et al. 2009 Clim Dyn) • Hydrological forcing and absorbing aerosol(Andrews et al. 2010 GRL; Ming et al. 2010 GRL) • Observed responses(Zhang et al. 2007 Nature; Wentz et al. 2007 Science; Allan and Soden 2008 Science, Adler et al. 2008 JGR; Wild et al. 2008 GRL; Zolina et al. 2010 GRL)

6. PREPARE proposal • Core datasets, including improvement of surface radiation budget using BSRN and empirical formula (WP1) • Quantifying trends and physical relationships (WP2) • Precipitation Extremes (WP3) • Modelling Experiments (WP4) • Implications for Climate Projections (WP5) • Hypothesis: • Current observing systems are inadequate for monitoring current changes in the hydrological cycle • Changes in aerosol, in particular absorbing aerosols, are key to understanding current changes in precipitation • Projected responses in extreme precipitation are primarily related to parametrizations and relate to present day bias in the probability distribution of precipitation events 2010 2012 2012 2013

7. PREPARE proposal • Core datasets, including improvement of surface radiation budget using BSRN and empirical formula (WP1) • Quantifying trends and physical relationships (WP2) • Precipitation Extremes (WP3) • Modelling Experiments (WP4) • Implications for Climate Projections (WP5) • Hypothesis: • Current observing systems are inadequate for monitoring current changes in the hydrological cycle • Changes in aerosol, in particular absorbing aerosols, are key to understanding current changes in precipitation • Projected responses in extreme precipitation are primarily related to parametrizations and relate to present day bias in the probability distribution of precipitation events

8. GPCP Ascent Region Precipitation (mm/day) Initial Results:Sensitivity to dataset Allan (2009) J. Clim • Robust response: wet regions become wetter at the expense of dry regions • Large uncertainty in magnitude of change: satellite datasets and models & time period TRMM John et al. (2009) GRL

9. PREPARE: Initial Results • Opposing trends in wet and dry regions of the tropics • Response to ENSO • Mechanisms: • Aerosol forcing? • Circulation changes? • Data inhomogeneity? Allan et al. (2010) Environ. Res. Lett.

10. 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.

11. Increase in intense rainfall with tropical ocean warming (close to Clausius Clapeyron)‏ • SSM/I satellite observations at upper limit of model range Model intense precipitation constrained by moist adiabatic lapse rate; responses highly sensitive to model-specific changes in upward velocities (O’Gorman &Schneider, 2009, PNAS; Gastineau & Soden 2009; Turner and Slingo, 2009 ASL).

12. Can we understand regional changes in water cycle? Water vapour Precipitation Allan and Zveryaev (2010) Int J Climatology

13. Binning Precipitation by ω-Ts CMIP AMIP Percentiles of global temperature (warmer -->)

14. Matthias…

15. Datasets (WP1) PREPARE

16. Current Plans • i) Consolidation/intercomparison/analysis of datasets (WP1/2) • Explore use of BSRN and SSM/I water vapour data to constrain decadal changes in surface fluxes; EOF analysis • ii) Calculation of moisture transports (WP2) • DryWet; Tropics Extra Tropics • iii) Evaluation of precipitation extremes: AR4/AR5 (WP2/3) • iv) Define/identify AMIP (or other) experiments (WP4) • Explore sensitivity to aerosol: fast responses • v) Explore available QUMP experiments (WP4) • Sensitivity to parametrization • vi) Links between surface radiation/Hydrological forcing (WP1/4) • See Andrews et al. 2010 GRL and Ming et al. 2010 GRL