Shigeo YodenDepartment of Geophysics
Kyoto UniversityJapan
ESF-JSPS Frontier Science Conference Series for Young Researchers« Climate Change »Nynäshamn Sweden 24-29 June 2006Session 5: Future climate change
The Use of Numerical Models to Understand Climate Variability
and Change
Shigeo YodenDepartment of Geophysics
Kyoto UniversityJapan
ESF-JSPS Frontier Science Conference Series for Young Researchers« Climate Change »Nynäshamn Sweden 24-29 June 2006Session 5: Future climate change
Internal Interannual Variabilityand Detectability of Climate Change
of the Stratosphere-TroposphereCoupled System
1. General Introduction
1.1 Climate change Global warming
warming in the tropospherecooling in the stratosphere
why cooling in the
stratosphere ?
effects of volcanic eruption
IPCC the 3rd report (2001)
Lower Stratosphere
Lower Troposphere
Global average temperature anomaly
1.2 Interannual variations of the S-T coupled system possible causes (Yoden et al. 2002; JMSJ Special Issue)
responses to external forcingssun, volcano, human being, ... (ENSO, ice, biomass, ...)
internal variationsstratospheric sudden warmings (SSWs), Quasi-Biennial Oscillation (QBO), ...
ENSO
StratosphericSudden
Warmings
[K]
North Pole
South Pole
daily temperature at 30 hPa for 19 years (1979-1997)annual cycle
periodic response to the solar forcingwhat causes North - South difference?
intraseasonal variationsstratospheric sudden warming (SSW) internal dynamical processes
interannual variationsmodulation of intraseasonal variationsexternal forcings
– solar cycle– volcano– human being trend– ......
South Pole(NCEP)
North Pole(NCEP)
North Pole(Berlin)
courtesy ofDr. Labitzke
seasonal variation of histograms of the monthly mean temperature (30 hPa)
Length of the global stratospheric observationis at most 5050 years.
Only numerical experimentsnumerical experiments can supply much longer datasets
to obtain statistically significant results,although they are not real but virtual.
The Earth Simulator
R&D Center
1.3 Numerical experiments on the interannual variations
Advances in computer technologyexponential growth in the last half century
computational speed and memory size
ENIAChttp://ei.cs.vt.edu/~history/ENIAC.Richey.HTML
http://www.es.jamstec.go.jp/esc/jp/index.html
OBSERVATIONS
DYNAMICAL MODELS
COMPLEX MEDIUM SIMPLE
EVOLVINGCONCEPTIAL MODELS
A schematic illustration of the optimum situation for meteorological research
Hierarchy of numerical models Hoskins (1983; Quart.J.Roy.Meteor.Soc.)
“Dynamical processes in the atmosphere and the use of models”
Three classes of the atmospheric modelssimple 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), ...
Balanced attack with these models is important !
JMA (1996)
2. The Use of Numerical Models to Understand Internal Variability and Climate Changein the Stratosphere-TroposphereCoupled System
2.0 Numerical experiments on the S-T interannual variations in our group in Kyoto for these two decadesLOM
Yoden (1987a,b,c) stratospheric sudden warmings (SSWs)Yoden and Holton (1988) quasi-biennial oscillation (QBO)Yoden (1990) seasonal march in NH and SH
MCMTaguchi, Yamaga and Yoden (2001) SSWs in S-T coupled systemTaguchi and Yoden (2002a,b,c) internal S-T coupled variationsNaito, Taguchi and Yoden (2003) QBO effects on coupled variationsNishizawa and Yoden (2005) spurious trends due to short datasetNaito and Yoden (2006) QBO effects on coupled variations
GCMYoden, Naito and Pawson (1996) SSWs in Berlin TSM GCMYoden, Yamaga, Pawson and Langematz (1999) a new Berlin GCMNishizawa, Nozawa and Yoden (2006) precip. in CCSR-NIES CGCM
2.1 Occurrence of stratospheric sudden warmings internal variability
polar vortex variation due to internal dynamicsstatistics ?characterization of the unprecedented year 2002 in the SH
ENSO
StratosphericSudden
Warmings
StratosphericSudden
Warmings
2002
Major stratospheric warming in the SH in 2002Hio and Yoden (2005, JAS Special issue )
Dynamical aspects of the ozone hole split in 2002association 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 H
emisphere 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”
SH-like NH-like3-dimensional Mechanistic Circulation Model
Monthly mean T (90N, 2.6 hPa)
reliable PDFs (mean, std., skewness, ....)
SH springnon-Gaussian long tail for extreme events
Taguchi and Yoden (2002b)Frequency distribution of the monthly mean temperatureat the pole, 2.6 hPa for 1000-year integrations
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
2.2 Influence of the QBO on the global circulation internal variability vs. response to “external” forcings
polar vortex variation due to internal dynamicsQBO in the tropics change at the side boundarymodulation of the polar vortex due to QBO ?
ENSO
StratosphericSudden
WarmingsInfluence of the QBOpropagation route of planetary waves
Gray et al. (2001; Baldwin et al., 2001, Plate 1)
Observations
Wallace (1973; AHL, 1987, Fig.8.2)latitude-height section of amplitude and phase of the zonal
wind QBOequatorial symmetryconstant downward propagation
Holton and Tan (1980, JAS ; 1982, JMSJ ) “Influence of the QBO on the global circulation ”
hemispheric data for 16 years
updated Holton-Tan relationship
Westerly phase Easterly phase
Polar vortex stronger, colder weaker, warmer
Major warmings 7 in 26 winters 13 in 20 winters
W – E GPH (JFM)
zonal mean thickness 100-300 hPa
W
E
E1.0 W1.0
~1K
Frequ
ency
(%
)
Temperature (K)
= 86N, p = 449hPa
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 temperaturein the tropospherein two runs: E1.0 and W1.0
Testing the difference between two averages
the large sample methoda standard normal variable Z
the probability that Z reaches 40.6 for two samples of the samepopulations is very small ( < 10-27 )
Naito and Yoden (2005, SOLA ) “Statistical analysis of the QBO effects on the extratropical strat
osphere 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 dynamicsincrease of GHGs cooling trend in Sdetectable for a finete (short) record ?
ENSO
StratosphericSudden
WarmingsAnthropogenic influences
cooling trend in Swarming trend in T
cooling trend in the stratosphere
IPCC the 3rd report (2001)
Lower StratosphereGlobal average temperature anomaly
Shine et al. (2003)
Nishizawa and Yoden (2005, JGR ) “Spurious trend in a finite length dataset with natural variability”
spurious trend vs. true trend
natural interannual variability of a coupled S-T model non-Gaussian PDFs
linear trend+
random variability
N=5N=5N=10N=20N=50
MCM(15,200years)2.6hPa
Detectability of cooling trend96 ensembles of 50-year integrationwith 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 vari
ability”data length to detect that with 90% statistical significance
J F M A M J J A S O N D
[hPa]1
10
100
1000
[years]
20
4060
6080
80
100
100
100
120
120
120
120
140
140
140
140
140
160
160
16018
0
220
180
stratosphere- 0.5K/decade(MCM;
15,200years)
troposphere0.05K/decade(AOGCM;
1,000years)
North Pole
3. Remarks for Further Discussion
Stainforth et al., 2005: Uncertainty in
predictions of the climate response to
rising levels of greenhouse gases.
Nature, 433, 403-406.
3.1 Hierarchy of numerical models Two types of climate change simulations
IPCChttp://www.ipcc.ch/ 3rd Assessment Report - Climate Change 2001high-end computers
Climateprediction.nethttp://www.climateprediction.net/ Trickling machines: 36,388 Completed runs: 76,503 at 27-Mar-2005 02:09:43 popular PCs
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 elaborationConceptual research versus hierarchy development
First-principles calculationFirst-principles calculationEmpirical formula
very long natural runensemble approach
3.3 Non-Gaussian PDFs examples
wind speed strong windprecipitation heavy rainrare but high-impact weather
reliable PDFvery long natural runensemble approach
New methods in statistical analysis
boot strapbreaking records
time-series analysis on record high (+) or low (x) in our 1520-year x 10 ensemble runs
Tack !