Transcript Storms, Drought and Wetness: Meteorological Extremes on different Time Scales Titelmasterformat durch Klicken bearbeiten 06.11.2015 Richard Blender Frank Sienz (PhD: extremes) Andrea Schneidereit (PhD: cyclones) Klaus Fraedrich KlimaCampus,

Storms, Drought and Wetness:
Meteorological Extremes on different Time Scales
Titelmasterformat durch Klicken bearbeiten
06.11.2015
Richard Blender
Frank Sienz (PhD: extremes)
Andrea Schneidereit (PhD: cyclones)
Klaus Fraedrich
KlimaCampus, Universität Hamburg, Germany, [email protected]
Workshop on metrics and methodologies of estimation of extreme climate events
UNESCO Headquarters, Paris, September 27-29
Topics
Midlatitude cyclones
– weather
Drought and wetness
– climate
Long term memory (1/f) – no time scale
Midlatitude Cyclones Extremes (North Atlantic)
Measures (maximum during life cycles)
• Central geopotential height, z1000 (GPH)
• Mean gradient of z1000
• Depth D,
• Radius R
• Vorticity ζ850
See also
Talks: Sergey Gulev, Gregor Leckebusch
Posters: Margarida Liberato, Natalia Vyazilova
... many others
Cyclone radius and depth: Resolution
T63
T42
T106
Higher resolution:
Increase of
• Weak & small
• Intense
cyclones
Schneidereit, RB, KF (2010)
More
Intense
Cyclone radius and depth in a warmer climate (A1B)
A1B
Negligible effect
Schneidereit, RB, KF (2010)
Return levels for cyclone extremes in 20C, A1B
Main effects in warmer climate (LLR and Akaike):
z1000, grad z, ζ850:
Scale parameter σ increases
Depth:
Shape parameter increases (ξ↗0)
Extreme intensities increase in A1B
Sienz et al. (2010)
NAO Impact - covariate
NAO classes (ERA-40)
NAO covariate for scale
Return levels for NAO: −2, ..., 2
β1 ERA-40
20C
‘stationary’
More intense cyclones for NAO > 0
Trend not significant as covariate
Sienz et al. (2010)
Akaike-test fails for D
Life cycles: Data collapse
Rescaled depth vs. rescaled age (idea: Rudeva and Gulev, 2007)
Winter
Summer
1. No exponential growth and decay phase
2. Consequences for extremes in self-similar processes?
Schneidereit, RB, KF (2010)
Summary - Cyclones
1. ERA40 and 20C (20CS) simulation agree
2. Distributions: climate change weak (<resolution effect)
3. Extremes: intensities increase
4. Weibull and Gumbel distributions
Extremes in drought and wetness conditions (SPI)
Precipitation averages → Standardized Precipitation Index SPI
months, seasons, years
Climate change assessment
Transform cdf of precipitation p (Γ1, left) to standard normal Φ, SPI = g(p)
Climate change: Γ2 (dashed, left), same transformation,
SPI’ = g(p’)
SPI’ is not standardized
Sienz et al. (2007)
SPI: Generalized Gamma distribution
Standard distribution: Γ-distribution for monthly mean precipitation
Sienz et al. (2005) find deficits in several regions, suggest
Generalized Gamma distribution Γ(x,b,k,d ) - additional parameter d
Includes Weibull, exponential, lognormal, Chi-squared
SPI in Iceland and climate change
Frequencies of SPI classes
CTL pre-industrial
20C present day
A1B warmer climate
-2
-1.5
Warm climate A1B:
Less dry, more wet extremes
-1
0
1
1.5
2
Sienz et al. (2007)
Extreme SPI in Iceland caused by cyclone anomalies
Cyclone densities in 20C (1959-2000, DJF)
Climate change: A1B - 20C wet
Wet, SPI ≥ 1.5: more cyclones
more cyclones
Sienz et al. (2007)
Dry, SPI ≤ −1.5: less cyclones
Extreme dry and wet conditions in a warmer climate
SPI Changes in extreme wet and dry classes in a warmer climate (A1B)
Wet W3
%
Dramatic changes - compare with P(X>2σ) = 2.3%
Dry D3
%
Summary - drought and wetness, SPI
SPI calculation
Generalized Γ-distribution for precipitation averages
Warmer climate
Increase of extremes (SPI > 2, < –2)
Midlatitudes: drought
Polar lats:
wetter
Tropics:
both
Impact of synoptic cyclones, large scale flow
Long term memory and extremes (nonstationary 1/f)
Long term correlations change
return time statistics (sketch)
1.0
Long term memory of observed & simulated temperature
Hurst exponent α
CRU
by DFA2
Detrended Fluctuation
Analysis
1-15yrs
Fluctuation function
F(t) ~ tα
ECHAM/HOPE
MPI/DKRZ,
Hamburg
1000 years
Power spectrum
S( f ) ~ f –β, β = 2α–1
Land: white
Sea: 1/f
1/f : α = β = 1
Fraedrich and Blender (2003)
Observations: TOGA COARE with 1/f spectra
TOGA/COARE R/V Kexue (Nov 92- Feb 92, 3.9S, 155.9E9) 1min resolution, 61 days
Yano JI, KF, RB (2004)
Spectrum
Detrended Fluctuation Analysis
1/f : β = α = 1
Blender et al. (2008)
Return time statistics and long term memory
Long term memory: Stretched exponential (Newell and Rosenblatt (1962),
Bunde et al. (2005); La Olla (2007))
γ = 1 – β, S(f ) ~
f -β
1/f-noise: Weibull distribution (Blender et al. (2008))
Confirmed by Santhanam
and Kantz (2008)
Return times sequence:
Long term memory (but weaker)
1/f-Spectrum and Gumbel distribution: Yangtze discharge
10
Fluctuations, rescaled
10
• intra-annual:
1/f
• inter-annual:
weak LTM
(x)
Yangtze discharge, daily
10
10
10
10
Wang et al. (2008)
10
10
10
(x)
4096
2048
1024
512
256
128
64
32
-2
-3
-4
-4
10
10
Yangtze (anom.)
0
-1
10
Antal et al. (2001):
Gumbel for 1/f
1
0
4
x
8
12
1
FAR d = 0.5 (1/f)
0
-1
-2
-3
-4
-4
0
4
x
8
12
Criticality and Gumbel distribution: Precipitation
Probability density of rescaled fluctuations (Bramwell et al., 1998, 2000)
First discovery:
Data Collapse
Turbulence (Re)
Second discovery:
Systems at criticality
Gumbel distribution
Precipitation
West China, daily
exp, uncorrelated
10
0

Gumbel
4096
2048
1024
512
10
-1
Prc Zhaosu
Xinjiang, daily
10
-2
-3
-2
-1
0
1
x
2
3
4
Gumbel distribution without extremes and correlations
5
Summary
Midlatitude cyclones
Distribution: resolution dependent, dominates climate change
Extremes: increased intensity
Droughts and wetness
SPI: recommend generalized Γ-distribution for precipitation average
Dramatic changes (depend on region)
Long term memory (1/f -noise)
Return times: Weibull distribution, Sequence shows long term memory
Extreme event distribution for discharge and precipitation
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06.11.2015
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