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 !