Detection of Human Influence on Extreme Precipitation 11 th IMSC, Edinburgh, July 2010 Seung-Ki Min 1, Xuebin Zhang 1, Francis Zwiers 1 & Gabi Hegerl 2 1 Climate Research Division, Environment Canada 2 School of GeoSciences, University of Edinburgh

2 Extreme Precipitation Changes  Expected to increase globally as the climate warms  Clausius-Clapeyron relationship (e.g., Allen and Ingram 2002, Held and Soden 2006)  larger changes than those in mean precipitation  Coupled model simulations project an increase of extreme precipitation over large parts of the globe (IPCC TAR, AR4)  Observed changes are qualitatively consistent with model projections (Groismann et al. 2005; Alexander et al. 2006; Hegerl et al. 2007)  Challenges to formal detection and attribution  Spatially and temporally limited observations  Large inter-model disagreements especially in the tropics (Kharin et al. 2005, 2007)  Scaling issue: “point” observations vs. “area mean” model estimates (Osborn and Hulme 1997, Chen and Knutson 2008)

3 Detectability Studies  Perfect/Imperfect model studies  Hegerl et al. (2004, J. Climate)  CGCM2 and HadCM3 (2xCO2 - 1xCO2)  “A signal-to-noise analysis suggests that changes in extreme precipitation should become more robustly detectable than changes in mean precipitation”  Min et al. (2009, Climate Dyn.)  ECHO-G 20 th century runs  Probability-based index to improve representativeness of area means of precipitation extremes and inter-comparison between observations and models  “Anthropogenic signals are robustly detectable over global/hemispheric domains, … largely insensitive to the availability of the observed data (based on HadEX) and to fingerprints from another model (CGCM3)”

4 This Study  Attempts to conduct formal detection analysis for extreme precipitations for  Real observations (HadEX)  Multi-model simulations (8 CMIP3 models)  Optimal detection technique  Probability-distribution based indices  Annual maximum daily (RX1D) and 5-day (RX5D) precipitations

5 Data  HadEX observations  Hadley Center global land-based climate extremes dataset (Alexander et al. 2006, JGR)  Based on 6000 stations and covers  CMIP3 models - 20C3M for  ANT (anthropogenic, 6 models, 19 runs)  ALL (natural plus anthropogenic, 5 models, 16 runs)  CTL (preindustrial control, 8 models, 106 x 50-yr chunks)  Preprocessing  Calculate extreme index on the original grid points  Interpolate them onto the same 5° × 5° grids  Consider grid points with more than 40-yrs observations during

6 CMIP3 Model Simulations Model nameANTALL CTL [# of 50-yr chunks] CCSM3*3410 CGCM35-20 CSIRO-Mk ECHAM5/MPI-OM3-10 ECHO-G*3326 GFDL CM GFDL CM PCM*4314 Models (Runs) 6 (19)5 (16)8 (106)

7 Probability Index (PI)  Convert 50-yr series of annual precipitation extremes into probability-based index (PI) ranging from 0-1 at grid-point base (1) Generalized extreme value (GEV) fit (2) Obtain PI time series P a - annual extreme of precipitation in year a. - location, scale, and shape parameters (fixed with time)

8 PI Trends  OBS: Overall increasing (some local decreasing over mid-lat Eurasia in RX5D)  ANT: increasing almost everywhere and reduced amplitude  ALL: similarly increasing in RX1D but noisier in RX5D OBS ANT ALL RX1DRX5D

9 PI Time Series NH mean 5-yr mean (centered)  OBS (black) - increasing trends, ANT/ALL - increasing but reduced amplitude  Stronger trends in RX1D than in RX5D (consistent with CC relationship)  Notable inconsistency between OBS and ALL in early 1950s RX1DRX5D ANT ALL

10 Detection Analysis  Analysis variables  Area-averaged 5-year mean extreme indices (PIs for RX1D or RX5D) for (NH, Nmid, Ntro  10 dimensions)  Space-time approach (Nmid + Ntro  20 dimensions)  Optimal regression (Allen and Stott, 2003)  Observations (y) are regressed onto model simulated “fingerprints” (x): y = β x + ε  Total least square methods  Detection: 5-95% range of β (scaling factor) > 0  Fingerprints (x) estimated from multi-model mean (ANT or ALL)  Internal variability ( ε) estimated from CTL runs  Dimension reduced to 6 leading EOFs -based on a residual consistency test (Allen and Tett, 1999)

11 Detection Results for PI  ANT detectable for both RX1D and RX5D  ANT signal robust for RX1D (detected when doubling internal variability, dashed)  ALL detectable only for RX1D and less robustly  ANT scaling factors near 2-3  model response to ANT underestimated RX1DRX5D

12 Sensitivity Tests  analysis  Excluding possible influence of ocean decadal mode (drying over North America in early 1950s)  ENSO residual observations  Excluding possible influence of ENSO, e.g peak  Model samplings  3 models that provide both ANT and ALL runs  Interpolation methods  Interpolating model datasets onto original HadEX grids (3.75º x 2.5º)  Overall results are insensitive (not shown)

13 Conclusions  Formal detection analysis for precipitation extremes using HadEX observations and IPCC AR4 multi-model ensembles  Standardized probability index for better comparisons  Human influence detectable significantly, providing an evidence for human contribution to the observed intensification of heavy precipitation events during the latter half of the 20th century  Models tend to underestimate the observed change, suggesting possible underestimation of future projections  Many caveats remain - observational uncertainty, model performance and structural uncertainty, and scaling issues