1. 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 The Use of Numerical Models to Understand Climate Variability and Change

2. 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

3. 1. General Introduction

4. 1.1 Climate change  Global warming warming in the troposphere cooling in the stratosphere why cooling in the stratosphere ? effects of volcanic eruption IPCC the 3 rd report (2001) Lower Stratosphere Lower Troposphere Global average temperature anomaly

5. 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),... ENSO Stratospheric Sudden Warmings

6. [K] North Pole South Pole  daily temperature at 30 hPa for 19 years (1979-1997) annual cycle  periodic response to the solar forcing  what causes North - South difference? intraseasonal variations  stratospheric sudden warming (SSW)  internal dynamical processes interannual variations  modulation of intraseasonal variations  external forcings –solar cycle –volcano –human being  trend –......

7. South Pole (NCEP) North Pole (NCEP) North Pole (Berlin) courtesy of Dr. Labitzke  seasonal variation of histograms of the monthly mean temperature (30 hPa)

8. Length of the global stratospheric observation 50 is at most 50 years. numerical experiments Only numerical experiments can supply much longer datasets to obtain statistically significant results, although they are not real but virtual.

9. The Earth Simulator R&D Center 1.3 Numerical experiments on the interannual variations  Advances in computer technology exponential growth in the last half century  computational speed and memory size ENIAC http://ei.cs.vt.edu/~history/ ENIAC.Richey.HTML http://www.es.jamstec.go.jp/esc/ jp/index.html

10. OBSERVATIONS DYNAMICAL MODELS COMPLEX MEDIUM SIMPLE EVOLVING CONCEPTIAL 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”

11.  Three classes of the atmospheric models simple Low-Order Model (LOM)  O(10 0 ~10 1 ) variables  for conceptual description Lorenz(1960,1963) medium Mechanistic Circulation Model (MCM)  O(10 4 ～ 10 5 ) variables  for understanding mechanisms Boville(1986) complex General Circulation Model (GCM)  O(10 4 ～ 10 7 ) variables  for quantitative arguments Phillips(1956), Smagorinsky et al.(1965),... Balanced attack with these models is important ! JMA (1996)

12. 2. The Use of Numerical Models to Understand Internal Variability and Climate Change in the Stratosphere-Troposphere Coupled System

13. 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

14. 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 ENSO Stratospheric Sudden Warmings Stratospheric Sudden Warmings

15. 2002  Major stratospheric warming in the SH in 2002 Hio and Yoden (2005, JAS Special issue )

16.  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/

17.  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

18.  Taguchi and Yoden (2002, JAS ) “Millennium integrations of a coupled S-T model” SH-like NH-like 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

19. Taguchi and Yoden (2002b) Frequency distribution of the monthly mean temperature at the pole, 2.6 hPa for 1000-year integrations Frequency distribution [%] xσ -3 -2 -1 Mean +1 +2 +3 +4 +5. -U 45S,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

20. 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 ? ENSO Stratospheric Sudden Warmings Influence of the QBO propagation route of planetary waves

21. 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 QBO  equatorial symmetry  constant downward propagation

22.  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 phaseEasterly phase Polar vortexstronger, colderweaker, warmer Major warmings7 in 26 winters13 in 20 winters W – E GPH (JFM) zonal mean thickness 100-300 hPa W E

23. E1.0 W1.0 ~1K Frequency (%) 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 temperature in the troposphere in two runs: E1.0 and W1.0 Testing the difference between two averages  the large sample method a standard normal variable Z the probability that Z reaches 40.6 for two samples of the same populations is very small ( < 10 -27 )

24.  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

25. 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 ? ENSO Stratospheric Sudden Warmings Anthropogenic influences cooling trend in S warming trend in T

26.  cooling trend in the stratosphere IPCC the 3 rd report (2001) Lower Stratosphere Global average temperature anomaly Shine et al. (2003)

27.  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=5 N=10 N=20 N=50 MCM(15,200years) 2.6hPa

28. 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.)

29.  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 J F M A M J J A S O N D [hPa] 1 10 100 1000 [years] 20 40 60 80 100 120 140 160 180 220 180 stratosphere - 0.5K/decade (MCM; 15,200years) troposphere 0.05K/decade (AOGCM; 1,000years) North Pole

30. 3. Remarks for Further Discussion

31. 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 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

32. 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

33. 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

34. Tack !