detection of human influence on extreme precipitation

13
Detection of Human Influence on Extreme Precipitation 11 th IMSC, Edinburgh, 12-16 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

Upload: nguyet

Post on 15-Jan-2016

39 views

Category:

Documents


0 download

DESCRIPTION

11 th IMSC, Edinburgh, 12-16 July 2010. Detection of Human Influence on Extreme Precipitation. 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. Extreme Precipitation Changes. - PowerPoint PPT Presentation

TRANSCRIPT

Page 1: Detection of Human Influence on Extreme Precipitation

Detection of Human Influence on Extreme Precipitation

11th IMSC, Edinburgh, 12-16 July 2010

Seung-Ki Min1, Xuebin Zhang1, Francis Zwiers1 & Gabi Hegerl2

1Climate Research Division, Environment Canada2School of GeoSciences, University of Edinburgh

Page 2: Detection of Human Influence on Extreme Precipitation

2

Extreme Precipitation Changes

Expected to increase globally as the climate warms Clausius-Clapeyron relationship (e.g., Allen and Ingram 2002, Held and Soden 20

06) 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)

Page 3: Detection of Human Influence on Extreme Precipitation

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 20th century runs Probability-based index to improve representativeness of area means of precipitati

on 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)”

Page 4: Detection of Human Influence on Extreme Precipitation

4

This Study

Attempts to conduct formal detection analysis for extreme precipitations for 1950-99

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

Page 5: Detection of Human Influence on Extreme Precipitation

5

Data

HadEX observations Hadley Center global land-based climate extremes dataset (Alexander et

al. 2006, JGR) Based on 6000 stations and covers 1951-2003

CMIP3 models - 20C3M for 1950-1999 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 1951-99

Page 6: Detection of Human Influence on Extreme Precipitation

6

CMIP3 Model Simulations

Model name ANT ALLCTL

[# of 50-yr chunks]

CCSM3* 3 4 10

CGCM3 5 - 20

CSIRO-Mk3.0 1 - 6

ECHAM5/MPI-OM 3 - 10

ECHO-G* 3 3 26

GFDL CM2.0 - 3 10

GFDL CM2.1 - 3 10

PCM* 4 3 14

Models(Runs)

6 (19) 5 (16) 8 (106)

Page 7: Detection of Human Influence on Extreme Precipitation

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

Pa - annual extreme of precipitation in year a.

- location, scale, and shape parameters (fixed with time)

1

exp exp , =0

; , ,

exp 1 , 0, 1 0.

x

F xx x

; , , ,a aPI F P

Page 8: Detection of Human Influence on Extreme Precipitation

8

PI Trends 1950-99

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

RX1D RX5D

Page 9: Detection of Human Influence on Extreme Precipitation

9

PI Time Series 1950-99NH 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

RX1D RX5D

ANT

ALL

Page 10: Detection of Human Influence on Extreme Precipitation

10

Detection Analysis

Analysis variables Area-averaged 5-year mean extreme indices (PIs for RX1D or RX5D) for 1

950-99 (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

= βx + 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)

Page 11: Detection of Human Influence on Extreme Precipitation

11

Detection Results for PI 1950-99

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

RX1D RX5D

Page 12: Detection of Human Influence on Extreme Precipitation

12

Sensitivity Tests

1955-1999 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. 1998 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)

Page 13: Detection of Human Influence on Extreme Precipitation

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