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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 !