Transcript Regime diagram

ESF-JSPS Frontier Science Conference Series for Young Researchers
« Climate Change »
Nynäshamn Sweden 24-29 June 2006
Session 5: Future climate change
The Use of Numerical Models
to Understand
Climate Variability
and Change
Shigeo Yoden
Department of Geophysics
Kyoto University
Japan
ESF-JSPS Frontier Science Conference Series for Young Researchers
« Climate Change »
Nynäshamn Sweden 24-29 June 2006
Session 5: Future climate change
Internal Interannual Variability
and Detectability of Climate Change
of the Stratosphere-Troposphere
Coupled System
Shigeo Yoden
Department of Geophysics
Kyoto University
Japan
1. General Introduction
1.1 Climate change
 Global warming
 warming
in the troposphere
 cooling
in the stratosphere
 why cooling
in the stratosphere ?
Global average temperature anomaly
Lower Troposphere
Lower Stratosphere
 effects of
volcanic eruption
IPCC the 3rd report (2001)
1.2 Interannual variations of the S-T coupled system
 possible causes (Yoden et al. 2002; JMSJ Special Issue)
 responses to external forcings
 sun, volcano, human being, ... (ENSO, ice, biomass, ...)
 internal variations
 stratospheric sudden warmings (SSWs),
Quasi-Biennial Oscillation (QBO), ...
Stratospheric
Sudden
Warmings
ENSO
 daily temperature at 30 hPa for 19 years (1979-1997)
 annual cycle
[K]
 periodic response to
the solar forcing
 what causes
North - South difference?
 intraseasonal variations
 stratospheric sudden
warming (SSW)
 internal dynamical
processes
North Pole
 interannual variations
 modulation of
intraseasonal variations
 external forcings
– solar cycle
– volcano
– human being  trend
– ......
South Pole
 seasonal variation of histograms of the monthly mean
temperature (30 hPa)
South Pole
(NCEP)
North Pole
(NCEP)
North Pole
(Berlin)
courtesy of
Dr. Labitzke
Length of the global stratospheric observation
is at most 50 years.
Only numerical experiments
can supply much longer datasets
to obtain statistically significant results,
although they are not real but virtual.
1.3 Numerical experiments on the interannual variations
 Advances in computer technology
 exponential growth in the last half century
 computational speed and memory size
The Earth Simulator
R&D Center
ENIAC
http://ei.cs.vt.edu/~history/
ENIAC.Richey.HTML
http://www.es.jamstec.go.jp/esc/
jp/index.html
 Hierarchy of numerical models
 Hoskins (1983; Quart.J.Roy.Meteor.Soc.)
“Dynamical processes in the atmosphere and the use of models”
OBSERVATIONS
COMPLEX
EVOLVING
CONCEPTIAL MODELS
DYNAMICAL MODELS
MEDIUM
SIMPLE
A schematic illustration of the optimum situation for meteorological research
 Three classes of the atmospheric models
 simple Low-Order Model (LOM)
 O(100~101) variables
 for conceptual description
Lorenz(1960,1963)
 medium Mechanistic Circulation Model (MCM)
 O(104～105) variables
 for understanding mechanisms
Boville(1986)
 complex General Circulation Model (GCM)
 O(104～107) variables
 for quantitative arguments
Phillips(1956), Smagorinsky et al.(1965), ...
JMA (1996)
Balanced attack with these models is important !
2. The Use of Numerical Models
to Understand Internal Variability
and Climate Change
in the Stratosphere-Troposphere
Coupled System
2.0 Numerical experiments on the S-T interannual
variations in our group in Kyoto for these two decades
 LOM
 Yoden (1987a,b,c) stratospheric sudden warmings (SSWs)
 Yoden and Holton (1988) quasi-biennial oscillation (QBO)
 Yoden (1990) seasonal march in NH and SH
 MCM
 Taguchi, Yamaga and Yoden (2001) SSWs in S-T coupled system
 Taguchi and Yoden (2002a,b,c) internal S-T coupled variations
 Naito, Taguchi and Yoden (2003) QBO effects on coupled variations
 Nishizawa and Yoden (2005) spurious trends due to short dataset
 Naito and Yoden (2006) QBO effects on coupled variations
 GCM
 Yoden, Naito and Pawson (1996) SSWs in Berlin TSM GCM
 Yoden, Yamaga, Pawson and Langematz (1999) a new Berlin GCM
 Nishizawa, Nozawa and Yoden (2006)
precip. in CCSR-NIES CGCM
2.1 Occurrence of stratospheric sudden warmings
 internal variability
 polar vortex variation due to internal dynamics
 statistics ?
 characterization of the unprecedented year 2002 in the SH
Stratospheric
Sudden
Warmings
ENSO
 Major stratospheric warming in the SH in 2002
 Hio and Yoden (2005, JAS Special issue )
2002
 Dynamical aspects of the ozone hole split in 2002
 association with the major stratospheric sudden warming
 Baldwin et al. (2003)
http://jwocky.gsfc.nasa.gov/
 Hio and Yoden (2005, JAS Special issue )
“Interannual variations of the seasonal march in the Southern
Hemisphere stratosphere for 1979-2002 and characterization
of the unprecedented year 2002”
 Scatter diagram between
upward EP flux
(45-75S, 100hPa,
Aug.16-Sep.30)
and
zonal-mean zonal wind
(45S, 20hPa,
Oct.1-15)
 an extreme event
with high-correlation
- 0.73  - 0.86
02
 Taguchi and Yoden (2002, JAS )
“Millennium integrations of a coupled S-T model”
 3-dimensional
Mechanistic
Circulation
Model
 Monthly mean
T (90N, 2.6 hPa)
 reliable PDFs
(mean, std.,
skewness, ....)
 SH spring
 non-Gaussian
 long tail
for extreme events
SH-like
NH-like
Frequency distribution [%]
xσ
-3
-2
-1 Mean
+1
+2
+3
+4
+5
.
-U45S,20hPa
4.2 4.2 58.3 20.8 8.3 0.0 4.2 0.0 0.0
Gaussian
2.1 13.6 34.1 34.1 13.6 2.1 0.1 3x10-3 -
T&Y(Feb.) 0.3 8.7 47.7 32.8 7.0 1.8 1.1 0.2 0.2
Taguchi and Yoden (2002b)
Frequency distribution of the monthly mean temperature
at the pole, 2.6 hPa for 1000-year integrations
2.2 Influence of the QBO on the global circulation
 internal variability vs. response to “external” forcings
 polar vortex variation due to internal dynamics
 QBO in the tropics  change at the side boundary
 modulation of the polar vortex due to QBO ?
Stratospheric
Sudden
Warmings
ENSO
 Observations
Gray et al. (2001; Baldwin et al., 2001, Plate 1)
 Wallace (1973; AHL, 1987, Fig.8.2)
latitude-height section of amplitude and phase of the zonal wind QBO
 equatorial symmetry
 constant downward propagation
 Holton and Tan (1980, JAS ; 1982, JMSJ )
“Influence of the QBO on the global circulation ”
 hemispheric data for 16 years
zonal mean thickness
W – E GPH (JFM)
100-300 hPa
E
W
 updated Holton-Tan relationship
Westerly phase
Easterly phase
Polar vortex
stronger, colder
weaker, warmer
Major warmings
7 in 26 winters
13 in 20 winters
 Naito, Taguchi and Yoden (2003, JAS )
“QBO effects on the S-T coupled variations”
 long time integrations with a MCM: N = 10,800 days
 frequency distributions
of the polar temperature
in the troposphere
in two runs: E1.0 and W1.0
 the large sample method
a standard normal variable Z
E1.0
Frequency (%)
 Testing the difference
between two averages
f = 86N, p = 449hPa
the probability that Z reaches
40.6 for two samples of the same
populations is very small ( < 10-27 )
W1.0
~1K
Temperature (K)
 Naito and Yoden (2005, SOLA )
“Statistical analysis of the QBO effects on the extratropical
stratosphere and troposphere”
 large samples of daily data (NCEP/NCAR reanalysis)
 ~2,000 days for each phase
2.3 Detectability of a trend
 internal variability vs. response to “external” forcings
 polar vortex variation due to internal dynamics
 increase of GHGs  cooling trend in S
 detectable for a finete (short) record ?
Stratospheric
Sudden
Warmings
Anthropogenic influences
cooling trend in S
warming trend in T
ENSO
 cooling trend in the stratosphere
Global average temperature anomaly
Lower Stratosphere
IPCC the 3rd report (2001)
Shine et al. (2003)
 Nishizawa and Yoden (2005, JGR )
“Spurious trend in a finite length dataset with natural variability”
 spurious trend vs. true trend
MCM(15,200years)
2.6hPa
linear
N=5 trend
+
N=10
random
variability
N=20
N=50
 natural interannual variability
of a coupled S-T model
 non-Gaussian PDFs
 Detectability of cooling trend
 96 ensembles of 50-year integration
 with external linear trend
-0.25K/year around 1hPa
Natural variability
small in summer (July)
large in winter (Feb.)
 Nishizawa, Yoden and Nozawa (2006, JGR )
“Detectability of true trend based on reliable PDFs of natural
variability”
 data length to detect that with 90% statistical significance
[hPa]
1
80
140
10
100
[years]
North Pole
140
120
100
220
160 140
120
100
60 80
20
60
stratosphere
- 0.5K/decade
(MCM;
15,200years)
40
troposphere
120
0.05K/decade
100
140 160
160 140
(AOGCM;
120
180
180
1000
1,000years)
J F M A M J J A S O N D
3. Remarks for Further Discussion
3.1 Hierarchy of numerical models
 Two types of climate change simulations
 IPCC
http://www.ipcc.ch/
 3rd Assessment Report
- Climate Change 2001
 high-end computers
 Climateprediction.net
http://www.climateprediction.net/
Trickling machines: 36,388
Completed runs:
76,503
at 27-Mar-2005 02:09:43
 popular PCs
Stainforth et al., 2005: Uncertainty in
predictions of the climate response to
rising levels of greenhouse gases.
Nature, 433, 403-406.
3.2 Held (2005, BAMS ) “The gap between simulation
and understanding in climate modeling”
 The need for model hierarchies
 The practical importance of
understanding
 Filling the gap
 The future of climate theory
 Elegance versus elaboration
 Conceptual research versus
hierarchy development
 First-principles calculation
 First-principles calculation
 Empirical formula
 very long natural run
 ensemble approach
3.3 Non-Gaussian PDFs
 examples
 wind speed  strong wind
 precipitation  heavy rain
 rare but high-impact weather
 reliable PDF
 very long natural run
 ensemble approach
 New methods in statistical
analysis
 boot strap
 breaking records
time-series analysis on
record high (+) or low (x)
in our 1520-year x 10
ensemble runs
Tack !