1 / 24

YODEN Shigeo Dept. of Geophysics, Kyoto Univ., JAPAN

August 4, 2004; SPARC 2004 Victoria 　＋ α － β for Colloquium on April 15, 2005. Large Ensemble Experiments on the Interannual Variability and Trends with a Stratosphere-Troposphere Coupled Model. YODEN Shigeo Dept. of Geophysics, Kyoto Univ., JAPAN. Introduction

chloe
Download Presentation

YODEN Shigeo Dept. of Geophysics, Kyoto Univ., JAPAN

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. August 4, 2004; SPARC 2004 Victoria　＋α－β for Colloquium on April 15, 2005 Large Ensemble Experiments on the Interannual Variability and Trends with a Stratosphere-Troposphere Coupled Model YODEN Shigeo Dept. of Geophysics, Kyoto Univ., JAPAN • Introduction • Internal variability obtained in large ensemble experiments • Experiments on the QBO effects on the S-T coupled variability • Experiments on the spurious trends due to finite-length datasets • Concluding Remarks

  2. 1. Introduction Causes of interannual variations of S-T coupled system ENSO (Yoden et al., 2002; JMSJ )

  3. Labitzke Diagram (Seasonal Variation of Histograms of the Monthly Mean Temperature; at 30 hPa) Only numerical experiments can supply much longer datasets to obtain statistically significant results, although they are not real but virtual. Length of the observed dataset is at most 50 years. North Pole (Berlin) South Pole (NCEP) North Pole (NCEP) courtesy of Dr. Labitzke

  4. 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 • Advancement of computers

  5. EVOLVING CONCEPTIAL MODELS OBSERVATIONS DYNAMICAL MODELS COMPLEX MEDIUM SIMPLE • Hierarchy of numerical models • Hoskins (1983; Quart.J.Roy.Meteor.Soc.) “Dynamical processes in the atmosphere and the use of models” A schematic illustration of the optimum situation for meteorological research

  6. Our research activity for these two decades • LOM: Low-Order Model • 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: Mechanistic Circulation Model • 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 • GCM: General Circulation Model • Yoden, Naito and Pawson (1996) SSWs in Berlin TSM GCM • Yoden, Yamaga, Pawson and Langematz (1999) a new Berlin GCM

  7. 2. Natural internal variability obtained in large ensemble experiments with an MCM • 3D global MCM: • Taguchi, Yamaga and Yoden (2001) • an atmospheric GCM • simplified physical processes • parameter sweep experiments • long-time integrations (max. 15,000 years)

  8. Monthly mean temperature (90N, 2.6 hPa) Labitzke diagram for 1000-year integrations • Taguchi and Yoden (2002b) • reliable PDFs • (mean, • std. deviation, • skewness, ...)

  9. zonal-mean zonal wind (45S, 20hPa, Oct.1-15) 02 upward EP flux (45-75S, 100hPa, Aug.16-Sep.30) • Use of reliable PDFs to evaluate the rarity of September 2002 in the SH • Hio and Yoden (2005, JAS Special issue, 62-3, 567-580)

  10. Frequency distribution [%] x-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 Monthly mean temperature (90N, 2.6 hPa)

  11. 3. Experiments on the QBO effects on the S-T coupled variability with an MCM • Perpetual winter integrations • Naito, Taguchi and Yoden (2003, JAS, 60, 1380-1394) • Naito and Yoden (2005) • “QBO forcing” in the zonal momentum eq.: : prescribed zonal mean zonal wind of a particular phase of the QBO • Assessment of the atmospheric response to a small (or finite) change in the external parameter by a statistical method.

  12. 10,800-day mean fields of zonal-mean zonal wind [m/s] 50m/s 75m/s 55m/s 45m/s 45m/s

  13. Time series of zonal-mean temperature [K]at φ=86N, p=2.6hPa for 2,000 days Total: 1,153 events

  14. Statistical significance • QBO effects on the troposphere • a large sample method • A standard normal variable: • The probability thatZ reaches 40.6 for two samples of the same populationsis quite small ( < 10-27 ) Frequency distributions of zonal-mean temperature [K] (86N, 449hPa, 10800 days) E1.0 W1.0

  15. Frequency distributions of zonal-mean T (90N, 200hPa, DJF for 1957-2002) W (2250 days) E (1800 days) • Observational fact • Naito and Yoden (2005, SOLA, 1, 17-20) • QBO effects on the polar troposphere

  16. 4. Experiments on the spurious trends due to finite-length datasets with internal variability • Nishizawa and Yoden (2005, JGR in press) • Linear trend • IPCC the 3rd report (2001) • Ramaswamy et al. (2001) • Estimation of spurious trend • Weatherhead et al. (1998) • Importance of variability with non-Gaussian PDF • SSWs • extreme weather events • We do not know • PDF of spurious trend • significance of the estimated value

  17. Linear trend • We assume a linear trend in a finite-length dataset with random variability • Spurious trend • We estimate the linear trend by the least square method • We define a spurious trend as N = 5 10 20 N = 50

  18. Moments of the spurious trend • Mean of the spurious trend is 0 • Standard deviation of the spurious trend is • Skewness is also 0 • Kurtosis is given by standard deviation of natural variability + Monte Carlo simulation with Weibull (1,1) distribution kurtosis of natural variability

  19. Probability density function (PDF) of the spurious trend When the natural variability is Gaussian distribution When it is non-Gaussian Edgeworth expansion of the PDF Cf. Edgeworth expansion of sample mean (e.g., Shao 2003)

  20. Cooling trend run • 96 ensembles of 50-year integration • with external linear trend • -0.25K/year around 1hPa Small STD Dev. Largest STD Dev.

  21. -0.1K/year -0.5K/year +0.1K/year t-test >99% >90% • Application to the real atmosphere data • 20-year data of NCEP/NCAR reanalysis • application of the model statistics

  22. How many years do we need to get statistically significant trend ? • - 0.5K/decade in the stratosphere • 0.05K/decade in the troposphere Necessary length for 99% statistical significance [years] 87N 47N

  23. How small trend can we detect in finite length data with statistical significance ? 50-year data 20-year data [K/decade] [K/decade]

  24. 5. Concluding Remarks • Recent advancement in computing facilities has enabled us to do some parameter sweep experiments with 3D MCMs • Very long-time integrations give • reliable PDFs (non-Gaussian, bimodal, .... ), • which might be important for nonlinear perspectives • in climate-change studies • Atmospheric response to small change in • an external parameter (e.g., QBO, solar cycle, …) • can be statistically assessed • by a large sample method

More Related