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An Analysis of the Column Ozone Trends A Commemoration to the 20 th Anniversary of the Montreal Protocol. S-K. Yang 1 , Craig S. Long 2 , Alvin J. Miller 2 , George Tiao 3 , Don Wuebble 4 1 Wyle IS/NOAA Climate Prediction Center, Camp Springs, MD

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An Analysis of the Column Ozone Trends A Commemoration to the 20th Anniversary of the Montreal Protocol

S-K. Yang1, Craig S. Long2, Alvin J. Miller2,

George Tiao3, Don Wuebble4

1Wyle IS/NOAA Climate Prediction Center, Camp Springs, MD

2NOAA Climate Prediction Center, Camp Springs, MD

3University of Chicago, Chicago, IL

4University of Illinois Urbana-Champagne, Champagne, IL

  • A short story between Taipei and Chungli
  • A fascinating story: The Finding of ozone holes
  • Not talked but good to know: ozone chemistry
  • Montreal Protocol, a climate success story, but has the “Mission Accomplished”?
objective and outline
Objective and Outline


  • Compilation of A Cohesive Ozone Dataset
  • Statistical Algorithm: Hockey-Stick model
  • The modulation of the natural variability
  • Conclusion


To estimate the trends and seeking the confidence of “recovery”

the challenge
The Challenge
  • Large natural variability
  • Inter-satellite calibration
sbuv 2 v8 cohesive total ozone data set
SBUV(/2) v8 Cohesive Total Ozone Data Set
  • Builds upon the work of Miller et al. 2004, in which total ozone observations from the NASA Nimbus-7 SBUV and the NOAA-9, 11, 14, 16, and 17 SBUV/2 are bias adjusted and combined to create a cohesive total ozone time series.
  • An overlap of at least one year is strived for. (Only the 6 month overlap between NOAA-11 and 16 is less than this criteria.) The mean difference between current and succeeding satellites for each zone is then computed. All satellites’ data are then adjusted to the Nimbus-7 data.
  • These observations are binned into 5 degree zones centered from 80N to 80S. Zonal means are computed for zones having more than 210 observations.
The monthly zonal mean total ozone anomalies from the long term mean. The monthly mean zonal total ozone values from the v8 Cohesive Total Ozone Data Set are used. Zones with no observations due to polar night are shown in black.

Figure 3a,b,and c shows the total ozone anomaly time series at 45N, the ‘global’ mean from 50N to 50S, and at 45S, respectively.

Figure 4. Hockey stick trend model showing an initial slope (1) to an inflection point set here to 1996, followed by a slope change (2) resulting in a post inflection point net slope (3). Ozone recovery is implied if the net slope is positive.
previous works i
Previous Works (I)
  • We have performed sensitivity studies upon the timing of the trend change and have found 1996 as the optimum year.
  • We have also shown that the initial slope, slope change and net slope are latitude dependant.
  • Sensitivity studies on deleting data after Mt. Pinatubo
previous work ii
Previous work(II)
  • The effects of Arctic oscillation (AO) and solar on the ozone trend and trend changes has been reported Miller et al. (2006), which focused on the variations of the Northern Hemisphere. Based on the 13 station data, they reported that the AO coefficients were quite consistent amongst all scenarios and statistically different from zero. The study indicates strongly that ozone variations are significantly modulated by AO and the stratosphere-troposphere linkage is an important aspect that must be included within the physical model.
  • To further validate the above argument, this current study expands to include the Southern Hemisphere, examining the effects of the Antarctic Oscillation (AAO) on the trend and trend changes.
previous mis understandings
Previous (mis)understandings
  • Miller et al. (2003) AO/AAO Vertical Motion vs. OLR -> cross-equator signals.
  • Miller et al. (2006) using Ozone sonde data -> modulation by solar cycle and QBO insignificant, when using Singapore wind as a regressor.
  • the QBO is a natural variation of meteorological parameters found mainly in the tropics, but with reduced amplitude and phase outside this area. It\'s period varies from about 24-33 months with an average period of about 26 months. Including the terms in  the equations accounts for some portion of the variability.
  • QBO induced secondary circulations could affect the extra-tropics through the changes in the planetary wave ducting (Schoeberl,, 2007) Using in situ measurements made by ozonesondes and satellite data, Logan, et al (2003) indicates that QBO affects column ozone for the levels above 40 hPa in the subtropics. The effects of QBO to the mid-high latitudes are more complicate with the latitudinal phase shifts. Using the Singular-Value Decomposition Randel and Wu (1996) isolates the global QBO signal and expressed it by a set of 2 orthogonal modes, SVD1 and SVD2, which can account for the latitudinal variations. In their analysis of a shorter time series from 1979 to 1994, the SVD1 and SVD2 account for 72% and 25% of the overall covariance, respectively.
attributes of the current study
Attributes of the Current Study
  • Cohesive SBUV/(2) Total Ozone Time Series, 1979~2007, Zonal 5deg resolut’n – by Craig Long
  • Hockey-Stick Model on 45N, 45S, and 50NS
  • Inflection at Jan 1996
  • Methodically adding/deleting number of regressors: Solar, AO, AAO, QBO(svd1+svd2) –> 6 combinations.

Monthly time series of the F10.7 solar cycle, Arctic Oscillation, Antarctic Oscillation, and QBO winds. The QBO winds is presented as two projections of the single value decomposition as described in Randel and Wu (1996).

Figure 5. Monthly time series of the F10.7 solar cycle, Arctic Oscillation, Antarctic Oscillation, and QBO winds. The QBO winds is presented as two projections of the single value decomposition as described in Randel and Wu (1996).

Figure 5. Monthly time series of the F10.7 solar cycle, Arctic Oscillation, Antarctic Oscillation, and QBO winds. The QBO winds is presented as two projections of the single value decomposition as described in Randel and Wu (1996).

unit du yr


Unit: Du/Yr


From 1979 to 1996, w1, Fig 1, shows that ozone decreases faster at 45N and 45S, in the range of [-1.3, -1.4] and [-1.18, -1.24] Du/Yr, respectively, as compared to the 50NS, [-0.68, -0.70] Du/Yr. The larger area of the tropics with the low variability contributes to the high confidence and lower reduction rate.
  • Similarly for the Slope Change w2, Fig. 2, from 1996 to 2007, are also higher at 45N [1.7, 1.9]and 45S [1.1, 1.3] . However, the magnitude of 45S is only comparable to 50NS [1.07, 1.11].



Fig 3, shows the net slope (w1+ w2) from 1996 on. This can be considered as the “current” trend, which shows reasonable recovery at the rate of [0.4, 0.54] Du/Yr at 45N. Slightly lower magnitude of [.39, .40] is for 50NS, while 45S is uncertain at [-.11, 0.06]. 50NS is the only one with statistical confidence at 95% level, while 45N is confident at 69% level.
  • Fig.4, The f10.7 solar index from NOAA/National Geophysical Data Center (NGDC) is used as the regressor. Fig. 4 shows that the solar coefficients are positive for the 45S [0.013, 0.015] and 50NS [0.016, 0.021], but negative for 45N[-0.0058, -0.0016] for all the cases. Considered at 95% confidence level, neither 45N, nor 45S are statistical significant. But, on the larger regional mean of 50NS, the coefficient is quite significant, at T > 4, even the magnitude of the coefficients are small. This result reflects that the solar is more of a “global” decadal modulator that could easily be masked by the stronger seasonality of the extra-tropics. This argument explains why using only the mid-high latitude station data on the northern hemisphere, Miller, et al., (2006) didn’t detect the significance of solar cycle.



The result from this statistical analysis shows that the AO and AAO coefficients are exclusively significant for 45N and 45S, respectively, (Fig 5 & 6) without inter-hemispheric correlations. Both AO and AAO are phenomena of the mid-high latitudes. With the correlation between the two indices at 2.1%, these results indicate that there is no dependency of one on the other.
  • Neither the coefficient of AO or AAO is significant for 50NS. The area average for 50N-50S is heavily weighted by the tropical regions (cos weighted) so these results indicate that the variation in latitudinal gradient between ozone and AO/AAO, which are primarily polar phenomena, is not statistically significant



The coefficients and SE of each component is displayed in Fig. 7 and Fig. 8. It is clear that SVD1 is fairly significant at 45S and 50NS on the beyond 3SE level, while marginal at 45N on 1 SE level. Meanwhile, the coefficient of SVD2 is mostly not significant, except at 45N on the 2 SE level.
  • By including these two regressors, all the correlations for the 45N, 45S and 50NS have improved by 1% to 3% with 50NS improved the most, by 3%; and the 45S as the second by 2%. Fig. 9
  • These results suggest that QBO could be a factor in the trend analysis, however, the effect of the latitudinal dependence need be included. Regression on a simpler index, such as Singapore wind, is apparently not sufficient.
  • Solar coeff. is significant at 50NS, but not 45N or 45S, suggests that the solar signal is masked by the large variations at the higher latitudes. However still effective in the global long-term variations.
  • AO coeff. is significant at 45N; AAO at 45S; not sig. 50NS, indicates that AO/AAO are phenomena of high latitudes, no cross hemispheric affects, no effect on the tropics.
  • QBO-SVD1, sig. at 45S and 50NS, but not 45N;
  • QBO-SVD2, marginally sig. at 45N, but not 45S or 50NS. (SVD1/SVD2 contributes 72%/25% of the QBO variances)
  • Adding QBO, increases R^2. suggests should be included in the regression of global data.
  • Ozone “Recovery” at 45S is iffy

Conclusions:-The net slope for the ‘global’ average is significantly positive for all combinations of the regressors. The net slope is positive at 45N for all regressors and significant for combinations including the QBO. -The net slope at 45S is neutral for the non-QBO regressor combinations and slightly negative for the QBO regressor combinations, suggesting weak confidence on SH ozone “recovery”.-The QBO regressor plays a significant role in the time variation of ozone anomalies, as well as AO and AAO, which are exclusively significant for the NH and SH, respectively, without inter-hemispheric correlations

  • SBUV/(2)v8 Cohesive Total Ozone Data Distribution:
chapman reactions
Chapman reactions


O2 + hv -> O + O (1)

O + O2 -> O3 (2)

(1/v = wavelength < ~ 240 nm)


O3 + hv -> O2 + O (3)

O + O2 -> O3 (2) as above


O + O3-> O2 + O2 (4)

production of chlorine radicals
Production of Chlorine Radicals

HCl + ClONO2 ->HNO3 + Cl2 (1)

ClONO2 + H2O->HNO3 + HOCl (2)

HCl + HOCl->H2O + Cl2 (3)

N2O5 + HCl->HNO3 + ClONO (4)

N2O5 + H2O->2 HNO3 (5)


ClO + NO2 + M->ClONO2 + M (6)

  • Return of sunlight
      • Cl2 + hv-> Cl + Cl
catalytic destruction of ozone
Catalytic Destruction of Ozone
  • (I)  ClO + ClO + M->Cl2O2 + M

Cl2O2 + hv->Cl + ClO2  

ClO2 + M->Cl + O2 + M

then:2 x (Cl + O3)->2 x (ClO + O2)   

net: 2 O3->3 O2 

  • (II)  ClO + BrO->Br + Cl + O2  

Cl + O3->ClO + O2  

Br + O3->BrO + O2   

net: 2 O3->3 O2

quantitle regressions
Quantitle Regressions:
  • A new effort has been initiated to analyze the trend using Quantitle regressions
  • Model:
  • Here, d indicates the potential breaking point. So b1represents the slope before the breaking point, b2 represents the change of slope after the breaking point.
Analysis of 45S
  • Estimation results from quantile regression
  • tau d (break point)0.11985.75-2.6274974 (-2.978768 -1.468426)2.1995501 (1.124589 2.657839)0.251990.583-1.55799 (-1.80705 -1.30672)1.04983 (0.51715 1.55038)0.51999.583-1.05657 (-1.15494 -0.95450)1.55962 (1.13143 1.92284)0.751999.75-1.14781 (-1.27143 -0.98804)2.32941 (1.51095 2.89432)0.92000.083-1.21080 (-1.31817 -1.03086)2.39273 (1.92351 3.10163)
  • Here tau is the percentile level. So tau=0.5 refers to the median. The 95% confidence interval for is given in the last column. That is for the change in slope at time d. It is clear in this case that the change in slope is significant at each percentile level tau.
  • One note about the change point d: The slope reversal at the lower tail of the distribution starts earlier. For example, the 25-th percentile starts to increase from mid 1990, but the median starts to increase from late 1999. Is this meaningful?
  • The break point for the low percentiles (tau at 0.1 and 0.25) might not be so informative. But the break point for the median at 1999 is several years after the break point for data at 45N. Is it true that the trend reversal started to rise earlier at 45N than at 45S?
Analysis of 45N
  • d (break point)0.11995.167-1.60095198 (-1.7846031 -1.293733)2.55876012 (1.8562309 2.992918)0.251995.333-1.28238739 (-1.4139918 -1.092146)1.78466922 (1.3961505 2.202969)0.51995.417-1.08341357 (-1.284877 -0.958955)1.30407378 (0.997340 1.519519)0.751995.083-1.49394417 (-1.6753551 -1.332436)1.75655403 (1.4624827 2.189303)0.91995-1.347469566 (-1.841944 -1.159466)1.486762805 (0.895225 2.370531)
  • For 45N, the break point occurs in 1995 at each percentile level. Again the slope reversal is statistical significant.
50n s
  • Average from 50S to 50N
  • d (break point)0.11993.5-0.96097264 ( -1.053768 -0.867350)1.37330194 (1.171348 1.53623189)0.251993.5-0.85813958 (-0.963342 -0.744805)1.16312144 (0.99253596 1.34556197)0.51993.583-0.84454957 (-0.908246 -0.766258)1.08858781 (0.96866115 1.20199493)0.751993.75-0.73690350 (-0.873604 -0.644839)0.90740143 (0.75221333 1.07860069)0.91994.417-0.76568348 (-0.808068 -0.717230)1.00465073 (0.867684 1.093863)
  • The break point for the average Dobson is around 1993, earlier than that for 45N.