detecting human influence on extreme daily temperature at regional scales
DESCRIPTION
Detecting human influence on extreme daily temperature at regional scales. Francis Zwiers 1 , Xuebin Zhang 2 , Yang Feng 2 1 Pacific Climate Impacts Consortium, University of Victoria, Victoria, Canada 2 Climate Research Division, Environment Canada, Toronto, Canada. - PowerPoint PPT PresentationTRANSCRIPT
WCRP Extremes Workshop - 27-29 Sept 2010 Detecting human influence on extreme
daily temperature at regional scales
Photo: F. Zwiers (Long-tailed Jaeger)
Francis Zwiers1, Xuebin Zhang2, Yang Feng2
1Pacific Climate Impacts Consortium, University of Victoria, Victoria, Canada2Climate Research Division, Environment Canada, Toronto, Canada
Outline
WCRP Extremes Workshop - 27-29 Sept 2010
• Introduction• Changes in the mean
state• An analog for extremes• Example application• Discussion
Photo: F. Zwiers
Introduction• The causes of changes in mean temperatures
have been robustly attributed on global and regional scales
• The literature on the causes of changes in extreme temperature is still developing
• Principal study was by Christidis et al (2005)– Warmest night ANT influence robustly detected– Coldest day, coldest night ANT less robustly
detected– Warmest day ANT not detected
• Recent work strengthens these results, extends to all four extreme temp indices and to regions
WCRP Extremes Workshop - 27-29 Sept 2010
Evaluateamplitudeestimates
Observations1946-56
Model
1986-96
XY Total least squares regression in reduced dimension space
XYFiltering and projection onto reduceddimension space
Evaluate goodness of
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WCRP Extremes Workshop - 27-29 Sept 2010
• Have a Gaussian setup
),(~| XXY
Space-time observations vector (decadal means)
Space-time signal matrix (one column per signal)
Vector of scaling factors
Covariance structure
YX
WCRP Extremes Workshop - 27-29 Sept 2010
From a statistical perspective
• Ultimate small sample problem• Fingerprints to look for are from models.
• Error covariance structure also from models (eg., control runs)
• Do generalized linear regression (optimize signal-to-noise ratio)
• Take some aspects of signal uncertainty into account using either a total least squares or errors-in-variables approach
WCRP Extremes Workshop - 27-29 Sept 2010
A few features
Photo: F. Zwiers
TXGEVXY ),,(~| Space-time vector of annual extremes
Space-time signal matrix (one column per signal)
Vector of scaling factors
Vector of scale parameters
Vector of shape parameters
YX
Note that these are vectors
WCRP Extremes Workshop - 27-29 Sept 2010
An extremes analogue
Photo: F. Zwiers
Photo: F. Zwiers (Arctic Tern)
• Amount of high quality, gridded daily data is limited
• Assume that the location parameter varies with time and space (e.g., due to external forcing)
• Assume that scale and shape parameters constant in time
• Unable to explicitly represent spatial or temporal dependence
• Unable to reduce dimension so as to include only scales where variability of extremes is well simulated
WCRP Extremes Workshop - 27-29 Sept 2010
A few features / limitations
• Typically have ensembles of 20th century simulations from a given model – M ensemble members M annual extremes per year
• Assume that signal changes slowly– If roughly constant within decades 10M annual extremes per
decade
• Fit GEV to these decadal samples at grid boxes• Retain the decadal fields of location parameter estimates
as signal• Average across multiple models to reduce signal
uncertainty• Currently consider only one signal at a time (either ALL or
ANT)
How do we get the signal?
WCRP Extremes Workshop - 27-29 Sept 2010
• Observed annual temperature extremes• TNn (annual minimum daily minimum temperature)• TNx (annual maximum daily minimum temperature)• TXn (annual minimum daily maximum temperature)• TXx (annual maximum daily maximum temperature)
– HadEX, compiled by Alexander et al. 2006– Dataset covers 1946-2000; we analyze 1961-2000
• Simulated annual temperature extremes– ANT – 7 models, 25 simulations– ALL – 3 models, 11 simulations
What extremes do we consider?
• Observations from HadEX, Alexander et al., 2006• Express signal in decade j at gridbox k as
• Note that same scaling factor β is used everywhere
• Obtain mle for β by profile likelihood technique
N
j l k
kjkkljk
k
k k
X
Nl
1
10
1
,,)1(10 ))}ˆ
(1log()1
1(
)log(10{
kjk ,̂
WCRP Extremes Workshop - 27-29 Sept 2010
How do we fit the GEV to obs?
• From internal variability– Block bootstrap on observations using 5-year blocks– For a given signal, resample residuals, re-estimate β
• Includes ENSO timescale• Does not include lower frequency timescales • Do not resample in space, so variability in re-estimated β’s
reflects effects of spatial dependence
• Signal uncertainty due to model simulated internal variability– Block bootstrap on model output
• Compare with approximate confidence intervals from profile likelihood
• Check goodness of fit
WCRP Extremes Workshop - 27-29 Sept 2010
How do we assess uncertainty?
Results: Global
TNn
TXn
TNx
TXx
Coldest night
Coldest day
Warmest night
Warmest day
WCRP Extremes Workshop - 27-29 Sept 2010
Results: Regional
WCRP Extremes Workshop - 27-29 Sept 2010
WCRP Extremes Workshop - 27-29 Sept 2010
TNn
TXn
TNx
TXx
Coldest night
Coldest day
Warmest night
Warmest day
Implied change in waiting times (1990’s vs 1960’s)
• Spatial smoothing of signal, scale, shape• Attempt to separate two signals• Unable to optimize• Need better approach for taking signal
uncertainty into account – what is the parallel to TLS/EIV?
• Should be able to calculate FAR directly• Potentially a constraint on future extremes
WCRP Extremes Workshop - 27-29 Sept 2010
Refinements / Discussion
WCRP Extremes Workshop - 27-29 Sept 2010
Photo: F. ZwiersThe End Photo: F. Zwiers