Q. J . R. Meteorol. Soc. (2001), 127, pp. 989-1003

The correlation between tropical total ozone and outgoing long-wave radiation

By VICTORIA WILLIAMS* and RALF TOUMI Imperial College, UK

(Received 6 January 1999; revised 5 October 2000)

SUMMARY Interannual variations in tropical column ozone, as observed by the Total Ozone Mapping Spectrometer, are

analysed for the period 1979-94 using linear regression to extract signals related to the solar cycle, quasi-biennial oscillation, and El Nifio Southern Oscillation (ENSO). Using time series of outgoing long-wave radiation (OLR) as a proxy for ENSO variations, rather than the widely used Southern Oscillation Index is found to improve the fit to observed ozone. The variability in the ozone and OLR datasets is investigated using empirical orthogonal function analysis. The most strongly correlated OLR and ozone modes exhibit ENSO-like variability.

KEYWORDS: Outgoing long-wave radiation Ozone

1. INTRODUCTION

The observed interannual variability of ozone over recent decades has largely been described in terms of a trend and forced variations on the time-scales of the solar cycle, the quasi-biennial oscillation (QBO) and the El Niiio Southern Oscillation (ENSO) (Zerefos et al. 1992; Randel and Cobb 1994). Additional effects have been identified after large-scale volcanic eruptions (Zerefos et al. 1994; Angel1 1997). In order to understand the spatial and temporal variations in the distribution of ozone it is necessary to determine the contribution made by each of these factors and to seek to explain the remaining residual signal. It is particularly important that all natural components of interannual variability are identified and understood before attempts are made to estimate anthropogenic influences on ozone.

Many of the existing studies investigating interannual ozone variability have used linear regression analysis of the observed data with indices for the QBO, the solar cycle and El Niiio. As the concentration of ozone in the lower stratosphere is high, variations in tropopause height will also be reflected in total-ozone observations. Pre- vious studies (e.g. Hood 1997; Ziemke et al. 1997) have indicated that the inclusion of geopotential-height data in regression analyses significantly improves the fit to ob- served column ozone. However, direct observations of geopotential are difficult to ob- tain, particularly over a sufficient time span and with adequate resolution to investigate correlation with interannual variations in other atmospheric parameters and, generally, reliance is made on the model-derived analysis datasets. In the extratropics, changes in the geopotential height will be reflected in outgoing long-wave radiation (OLR) obser- vations because, in regions of enhanced convection, the tropopause height will be raised and the observed OLR will be reduced. Global satellite observations of OLR, such as the National Oceanic and Atmospheric Administration (NOAA) dataset (Liebmann and Smith 1996) used here, are readily available over the time period of the Total Ozone Mapping Spectrometer (TOMS) data. This allows the correlation between two directly observable properties to be investigated. Here the use of OLR as an indicator of ozone

* Corresponding author: Space and Atmospheric Physics, Imperial College of Science, Technology and Medicine, Prince Consort Road, London SW7 2BW, UK. @ Royal Meteorological Society, 2001.

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variations is investigated. The connection between the behaviour of OLR and tropo- spheric circulation suggests that observations of OLR could be used as an improved index of ENSO variability.

Previous studies (e.g. Randel and Cobb 1994) have modelled ENSO Variability using the Southern Oscillation Index (SOI). This index is given by the difference between monthly mean sea-level pressure at Tahiti and Darwin. Clearly, although large El Niiio events can sharply perturb this index, the localized nature of the index means that it may not effectively represent variability either at specific locations or across the tropical region as a whole. Here the possibility of including tropical-mean OLR variability, rather than SOI, is investigated. El Niiio events are known to perturb convective activity (Chandra et al. 1998). Such changes would be expected to be reflected in the dataset of observed OLR. In addition to the improvement of fit for the ENSO variation by providing a more realistic index of the circulation changes during an El Niiio event, OLR could also be expected to ‘explain’ ozone changes through other mechanisms. The distribution of ozone is sensitive to net radiative heating rates in the atmosphere (e.g. Haigh 1984; Williams and Toumi 1999), so the inclusion in the calculations of any factor affecting those heating rates could be expected to improve the fit to the observed values. In addition, the OLR dataset would be expected to respond to large-scale changes in the amount or type of cloud cover. Previous two-dimensional model calculations have suggested that such changes in cloud cover could impact on the global distribution of ozone (Williams and Toumi 1999). This relationship to cloud would also be expected to improve the ENSO fit by allowing the impact of cloud changes occurring during El Niiio (as described by Kent et al. 1995) to be directly incorporated into the regression fit.

Caution must be taken in the analysis of the correlation between TOMS ozone and OLR as there is an inherent relationship due to the satellite retrieval method, which may not be fully corrected in the data product. The relationship exists because the measured ozone column is the above-cloud amount, so in regions of intense cloud the observed total ozone is reduced compared with a clear-sky measurement. The data is adjusted to allow for this effect by adding an estimated amount of ‘below-cloud ozone’ based on climatological data. It has been argued (e.g. Thompson et al. 1993) that this method does not adequately adjust the data in the case of some lower-level clouds because it assumes a cloud height that is too high. This results in an overestimated tropospheric- ozone adjustment and so an overly high column value. It is possible that should there exist a systematic error in the climatology for tropospheric ozone, a false correlation would be seen between OLR and ozone. For example, if the satellite correction for ozone in the presence of cloud consistently gives an anomalously high value, a negative correlation would be found such that high ozone values coincide with low OLR.

Links between cloud cover and ozone distribution indicated by modelling studies (e.g. Dessler et al. 1996; Williams and Toumi 1999) are based on physical mechanisms, such as the changes in lower-stratospheric radiative heating rate in the presence of high cloud resulting in changes in the model circulation. As such, it can be expected that such an effect would be represented in the OLR-ozone correlation in addition to any artificial effect due to the data retrieval method.

Regression calculations to fit tropical-mean TOMS ozone for solar cycle, QBO and ENSO variability are discussed in section 3. The effect of using observed tropical-mean OLR data in place of the currently used SO1 to model ENSO variations is investigated. In order to further investigate the nature of the relationship between OLR and ozone, the principal components of the local variability in both datasets are identified and the correlations between them discussed in section 4. Section 5 describes the relationship between OLR and geopotential heights.

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Figure I . De-seasonalized tropical-mean anomalies (20"s to 20"N) in Total Ozone Mapping Spectrometer (TOMS) ozone (solid line) and National Oceanic and Atmospheric Administration (NOAA) outgoing long-wave radiation (OLR) (dotted line) for the period 1979-94. Values are smoothed using a 1-2-1 filter to remove short- time-scale variations. Calendar months with less than 20 days of available ozone data (due to gaps in the Meteor-3 retrievals) are treated as missing and are not incorporated into the de-seasonalization method, or into subsequent

calculations.

An additional intrinsic relationship between ozone and OLR exists as a result of the direct absorption of long-wave radiation by ozone. As a result, changes in the ozone amount could be expected to directly influence the amount of long-wave radiation reaching the top of the atmosphere. This effect is discussed in section 6.

2. DATA AND ANALYSES

Time series of monthly mean Version 7 TOMS total ozone (McPeters et al. 1996) are analysed for the period 1979-94. Data were supplied by the British Atmospheric Data Centre from the Nimbus-7 and Meteor-3 TOMS instruments. Towards the end of the analysis period, in 1993/94, the available data is limited by gaps in the Meteor-3 retrievals. All calendar months with less than 20 days of data are treated as missing and are, therefore, excluded from the regression analysis. Interpolated OLR data from the Advanced Very High Resolution Radiometer instruments on NOAA polar-orbiting satellites (Liebmann and Smith 1996) were provided by the NOAA Cooperative Institute for Research in Environmental Sciences (NOAA-CIRES) Climate Diagnostics Center, Boulder, Colorado, from their website*.

For the initial analysis, mean data for the region 20"N to 20"s were used. To illustrate the scale of the variations occurring, smoothed time series of de-seasonalized observed ozone and OLR for this region are shown in Fig. 1. For each of these datasets, the seasonal cycle was determined by averaging data over the 1979-94 period for each * www.cdc.noaa.gov (August 1998).

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Figure 2. W m-*Hz-'). (b) 30 mb monthly mean equatorial wind from the Climate Data Assimilation System re-analysis data (m s-I). (c) Southern

Oscillation Index (monthly mean sea-level pressure difference between Tahiti and Darwin (mb)).

Time series of (a) 10.7 cm solar radio flux (1 solar flux unit (s.f.u.) =

calendar month (e.g. all the January values). This seasonal cycle was then subtracted from the time series to provide an anomaly dataset. The new values were then smoothed using a 1-2-1 filter in time to remove short-time-scale variations.

The linear regression analysis applied is adapted from that used by Randel and Cobb (1994) and is such that the observed ozone is given by

O3(t) = a SOL + /? QBO + y . ENS0 + residual. (1)

No trend term is included in this analysis as Randel and Cobb (1994) and Stolarski et aZ. (1991) found no significant trend in ozone for the tropical region. To fit seasonal variability, the coefficients a,

(Y = A 1 + A2 cos ot + A3 sin ot + A4 cos 2ot + A5 sin 2ot

and y are each of the form:

+ A6 cos 3wt + A7 sin 3wt (2)

with o = 21r/12 months. As each term in Eq. (1) has 7 coefficients to be determined, there are a total of 21 coefficients to be fitted to the observed ozone time series. The

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solar cycle is represented by the time series of monthly mean 10.7 cm solar radio flux, shown in Fig. 2(a). The QBO is described by monthly mean equatorial winds at 30 mb, from the Climate Data Assimilation System re-analysis data (Kalnay et al. 1996) (Fig. 2(b)). Comparisons with rawinsonde observations from Singapore have suggested that the wind extrema in this dataset may be weaker than that observed (Pawson and Fiorino 1998). However, the nature of the calculations is such that, provided that this is a systematic error within the data and that the period of the oscillation is adequately reflected, the use of low wind values would simply increase the calculated sensitivity of ozone to a specified wind change. The proportion of ozone variability attributed to the QBO would not be significantly changed. ENSO variations are initially fitted using the SOI, given by the difference between monthly mean sea-level pressures at Tahiti and Darwin (Fig. 2(c)). To allow a comparison between the use of SO1 and the use of OLR as a method for fitting the ozone variations associated with El Niiio, the regression fit was then repeated using OLR data averaged over the same region (20"N to 20"s).

No signal is included to fit volcanic-induced variations in ozone. The time series of ozone data for the regression analysis includes the periods following the eruptions of El Chichon (April 1982) and Mount Pinatubo (June 1991). As these two events occur at an interval comparable to the length of the solar cycle, both coinciding with solar maximum, and given that the length of the time series under analysis allows only one complete solar cycle to be observed, no attempt is made to distinguish between solar cycle and volcanic effects. Angel1 (1997) describes a method for separating the solar and volcanic effects using a much longer time-scale dataset than that used here.

It should be noted that eliminating the solar cycle entirely from the regression calculations would have no effect on the coefficients calculated for the other signals, but would simply result in a larger residual signal between the observed and fitted ozone.

Experiments were carried out to investigate whether temporal lagging of the data would improve the fit. Each signal in the data was allowed to precede or follow the ozone signal by up to six months. The calculations here contain no lagging as no significant improvement in the fit was found.

3. REGRESSION CALCULATIONS

Using the regression analysis described above, the calculated total-ozone regression fits for the solar cycle, QBO and ENSO compare well with those calculated by Randel and Cobb (1994). Substituting OLR for SO1 in the regression fit calculation is found to improve the fit to observed ozone. The residuals for the two fit methods are shown in Fig. 3 . It can be seen that the fit to observed ozone is particularly improved in 1987 and 1991/92. These two periods correspond to El Niiio events that are not strongly indicated in the SO1 time series. It should be noted that there are time periods outside of these dates where the magnitude of the residual signal is increased by the use of OLR in place of SO1 in the regression fit. The root mean square of the residual over the time period shown is reduced from 2.24 Dobson units (DU) to 1.98 DU when OLR is included in place of SOI. The SO1 contribution to the initial regression fit is found to describe 4% of the tropical-mean ozone variance. When this component is removed and replaced by OLR observations, the proportion of the ozone variance described by the OLR term is 7.5%. However, the change in the total amount of ozone variance explained by the combined terms in the regression fit is not significantly changed by the introduction of the OLR term in place of SOI. The changing of the terms impacts on the proportions of ozone variability attributed to both the QBO and solar terms, such that the amount

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Figure 3. Solid line shows the residual of tropical-mean Total Ozone Mapping Spectrometer (TOMS) ozone after fitting for the solar cycle (10.7 cm solar radio flux), quasi-biennial oscillation (QBO) (equatorial-mean wind at 30 mb) and El Niiio Southern Oscillation (Southern Oscillation Index). The dotted line shows the residual of tropical-mean TOMS ozone after fitting for the solar cycle, QBO, and outgoing long-wave radiation. Ozone

residuals are in Dobson units (DU). Regression fits were carried out on datasets averaged over 20"N to 20's.

of ozone variance attributed to the QBO signal is higher for the fit including OLR than for that including SOI, with a reverse effect seen for the solar signal.

Figure 4 shows the annual cycle in the sensitivity of ozone to OLR as calculated from the linear regression analysis. It should be noted that this 'sensitivity' does not suggest that the ozone changes directly in response to changes in the OLR, but that changes in the two parameters occur such that, for example, in August an OLR of 1 W m-2 above the average for August is correlated with an ozone value 1.5 DU above the August average. To put this into the context of the observed variations, typical OLR anomalies in the tropical mean for August are less than 2 W mP2 with around half the values below 1 W mP2 for the time period considered. Typical August total-ozone anomalies are below about 5 DU. This suggests that the addition of OLR could provide the explanation for a proportion of the ozone variation but, clearly, more statistical analysis is required to determine the nature and the significance of any identified improvement in the fit.

The calculated ozone effect attributed to changes in OLR is considerably larger than the modelled calculations for the radiative effect of high cloud on ozone (Williams and Toumi 1999). This suggests that the observed correlation cannot be attributed to the cloud effect alone.

The limitations identified in the TOMS treatment of cloud must also be considered (Thompson et al. 1993). For the results shown to occur purely due to error in the correction for tropospheric ozone in cloudy conditions, the climatology would have to systematically underestimate below-cloud ozone, such that the average observed total ozone for each point was about 5% lower. The use of tropical average, monthly mean data means that each data point will include observations for both clear and cloudy conditions. The systematic error in the tropospheric-ozone climatology would,

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Figure 4. Annual cycle in ozone sensitivity to outgoing long-wave radiation (OLR) calculated from a linear regression fit of tropical-mean data (20"s to 20"N). Units are Dobson units (DU) per W m-*. Values correspond to the calculated relationship between Total Ozone Mapping Spectrometer ozone and National Oceanic and Atmospheric Administration OLR anomalies when OLR is incorporated in an ozone regression model containing

signals for the solar cycle and quasi-biennial oscillation.

therefore, need to be much greater than 5% of the column-ozone value to produce such an effect. As tropospheric ozone accounts for only around 10% of the observed column value, errors in the climatology would have to underestimate tropospheric ozone values by about 50% in order to entirely explain the effect observed. This is in contrast to the retrieval difficulties suggested by Thompson et al. (1993), which suggested a possible overestimation of ozone due to inaccurate assumptions of cloud height.

Removing the seasonal cycle in the data for the ozone and OLR, precludes the study of features such as the maximum in column ozone observed in the southern tropical Atlantic in August to November which is suggested to be linked to increases in photochemical ozone formation from trace gases emitted by biomass burning over South America and Africa (Fishman et ul. 1990; Thompson et al. 1993). Such recurring annual events are removed from the analysis to allow the interannual structures to be considered.

The use of tropical-mean data in analysing the relationship between ozone and OLR is clearly limited in the scope of the results obtained. Interannual changes in OLR are largely localized, so local variances will be much greater than the variance in the tropical-mean OLR signal and much of the variability will be lost. In addition, as ENSO is characterized more by longitudinal variations likely to cancel out in the taking of any zonal mean, it is likely that the use of a more localized proxy than either the SO1 or tropical-mean OLR changes could improve the modelling of the ENSO effect. The relationship between localized OLR and ozone variations over the time period 1979-95 is investigated using empirical orthogonal function (EOF) analysis.

In order to verify that the regression method used above is appropriate for fitting the observed ozone signal, it is necessary to ensure that the added OLR term can explain a separate and distinct portion of the observed ozone signal from that previously explained by the terms for the solar cycle and QBO. The possibility that OLR values may be influenced by other factors in the regression fit is of crucial importance. Such relationships would influence the determined coefficients and could artificially

996 V. WILLIAMS and R. TOUMI

identify a relationship between ozone and OLR. Possible mechanisms for the existence of a significant influence on OLR on the time-scales of the other fitted cycles include the suggestion of a QBO signal in tropical deep convection (Collimore et al. 1998), and the identification of a solar-cycle variation in lower-stratospheric geopotential heights (van Loon and Labitzke 1998). The use of EOF analysis allows independent components of the variability in ozone and OLR to be identified.

4. STATISTICAL ANALYSIS

Singular value decomposition (SVD) is used to separately analyse maps of tropical ozone and OLR. Both are calculated on a 5" x 5" grid for the period 1979-94.

Data used here are not de-seasonalized. In order for a mode of variability to be identified, it must be characterized by both a time series and an accompanying spatial pattern. The use of the de-seasonalization method adopted by previous authors for regression calculations (e.g. Randel and Cobb 1994) and in the regression calculations described above was found to be inadequate for the effective removal of the seasonal signal from data to be used for EOF analysis. This resulted in the identification of artificial signals in the data and the degradation of expected independent signals, particularly the QBO. As the purpose of the EOF method adopted is to identify the principal spatial and temporal components of variability in the data, seasonal signals will be extracted in one or more of the components. In the case of ozone, the time series components 0, 1 and 3 show clear annual variability. Component 2 exhibits a high ( r = 0.67) correlation with the monthly mean equatorial winds at 30 mb, as used in the regression calculations above to fit for QBO variability. The corresponding spatial pattern compares well with that calculated for the QBO in ozone determined using other methods.

In the OLR data, where component 0 is the mean signal and component 1 exhibits annual variability, component 2 exhibits variability suggestive of El Nifio in both space and time. As shown in Fig. 5(a), the time component shows negative peaks coinciding with the El Nifio events of 1982/83, 1986/87 and 1991/92. The associated spatial pattern shows a strong variation with longitude across the Pacific. This component is found to correlate significantly with components 4, 5 and 7 of the TOMS data. These three components describe 4.9%, 3.6% and 2.2% of the variance outside component 0, respectively. Clearly, of these component 4 is the most significant as by definition it explains a higher proportion of the variance. In addition, component 4 of the TOMS data is most strongly correlated with component 2 of the OLR data. The time and space variability for component 4 of the TOMS data are shown in Fig. 5(b). From the time series corresponding to component 4, it can be seen that the variability described is strongest during the 1982/83 and 1991/92 El Nifio events, while the 1986/87 event is not strongly associated with this pattern of variability. The corresponding spatial pattern shows an east-west dipole structure with a nodal longitude near the dateline, as is seen in studies of the ozone response to El Nifio (Shiotani 1992). Component 5 (shown in Fig. 5(c)) indicates all three of these events with roughly equal intensity, while component 7 (shown in Fig. 5(d)) indicates a spatial pattern apparently associated with the early part of the 1982/83 event. It is important to note that the results suggest that the spatial characteristics of the ozone response to El Nifio differ not only between events but may also change as an event progresses. This would make the use of any regression method in the analysis of such an effect particularly restrictive.

This use of EOF analysis shows the limitations of using SO1 in regression fits for tropical ozone. Indeed, the results suggest that the El Niiio effect on ozone is too

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Figure 5. Time and space patterns of components. The product of the two patterns describes the mode of variability in W m-2 for outgoing long-wave radiation (OLR) and in Dobson units for ozone. (a) Component 2 of OLR data. (b) Component 4 of Total Ozone Mapping Spectrometer (TOMS) ozone data. (c) Component 5 of

TOMS ozone data. (d) Component 7 of TOMS ozone data.

complex to be fitted using any single tropical-mean parameter. Considering the localized ozone data, the behaviour is complex, with several components of the variability apparently linked to ENSO.

In addition, difficulties arising from the use of a de-seasonalization method such as that used by Randel and Cobb (1994) are clear. Removing the mean for each calendar month in the data series does not efficiently remove the seasonal cycle. The

998 V. WILLIAMS and R. TOUMI

use of this method before carrying out the EOF analysis was found to have two main effects on the derived principal components. Firstly, the correlation between the 'QBO' ozone and the time series of 30 mb equatorial-mean winds was severely degraded, as a portion of variability associated with the QBO was removed in the de- seasonalization procedure. This also impacts on the significance attributed to the QBO in ozone variability. Secondly, the failure of this method to adequately remove the seasonal cycle in ozone was found to result in the identification of an artificial periodic signal in ozone.

5 . CORRELATION BETWEEN OUTGOING LONG-WAVE RADIATION A N D GEOPOTENTIAL

In order to further understand this improvement in the fitted ozone when OLR is added to the regression fit, it is necessary to consider the factors contributing to interan- nual OLR variations. The OLR amount can be affected by various mechanisms and in order to understand why ozone changes are related to variations in OLR it is necessary to determine which processes govern the changes in OLR. In particular, the relationship between OLR and geopotential height is investigated here to determine the extent to which OLR can be used to represent changes in geopotential height. Correlations be- tween ozone and geopotential height have previously been identified outside the tropics (Hood 1997; Ziemke et al. 1997). It is important to identify whether the OLR-ozone relationship identified in the regression analysis is related to the links identified between ozone and geopotential height. If so, this would allow more widespread analysis of the variability identified in studies using geopotential height to be carried out. Such studies would then be able to incorporate directly observed data from satellite observations of OLR, which provide global coverage over a time-scale similar to that for which satellite ozone observations are available.

Here, a regression model similar to that described in section 2 is used to analyse the interannual variations in OLR. The regression is carried out such that the OLR time series is represented by:

(3 1

HEIGHT

OLR(t) = a . Z200mb(t) + B . Z 2 0 m b ( t ) + residual

where 2200 mb and 2 2 0 mb are geopotential heights at 200 and 20 mb, respectively, and the coefficients a and B are again of the form:

a = A , + A2 cos wt + A3 sin wt + A4 cos 2wt + A5 sin 2wt 4- A6 cOS 3wt + A7 sin 3wt (4)

with the A values being the coefficients to be determined by the linear regression. Geopotential-height data are taken from the National Centers for Environmental Pre- diction (NCEP) re-analysis. Geopotential heights for 200 and 20 mb were used as these were the levels at which Ziemke et al. (1997) identified maximum correlations with ozone, though it should be noted that these results were obtained at mid latitudes, and they identified no significant correlation between NCEP geopotential height and TOMS ozone in the tropics.

As variations in geopotential height and OLR are predominantly localized in nature, the fit was carried out for each point on a 5" x 5" grid within the tropical region (20"s to 20"N). Calculated values for the average percentage of OLR variance explained were determined for each of the terms in Eq. (3). The geopotential-height time series were found to account for 11% (200 mb) and 10% (20 mb) of the observed OLR variance. These values correspond to the average calculated over the 576 blocks.

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Figure 6. Annual cycle in outgoing long-wave radiation (OLR) sensitivity to geopotential height calculated from the means of linear regression fits for 5" x 5" blocks of tropical-mean data (20"s to 20"N). Units are W m-* gpm-' . Values correspond to the calculated relationship between National Oceanic and Atmospheric Administration OLR anomalies and National Centers for Environmental Prediction geopotential height at 200 mb

(solid line) and at 20 mb (dotted line).

The annual cycle in the mean calculated sensitivity of OLR to geopotential height at both 20 mb and 200 mb can be seen in Fig. 6. It can be seen that, as expected, the magnitude of the sensitivity of OLR to 200 mb geopotential height (solid line) is much greater than to 20 mb geopotential height (dotted line). Small changes in height at 200 mb would be expected to exert a greater influence on the temperature structure, and hence on the OLR, while to induce the same OLR change, a large change in 20 mb geopotential height would be required. Considering the magnitude of the attributed OLR changes rather than the sensitivity in W mP2 gpm-', the two heights contribute roughly equally. The OLR sensitivity to 200 mb geopotential height drops to a minimum in May/June, while the 20 mb sensitivity peaks at this time.

Maps of the percentage of OLR variance explained by the geopotential-height fields are shown in Fig. 7. The geographic variability in the geopotential-height-OLR rela- tionship is clear. There are large geographic variations in OLR sensitivity to changes in geopotential height and the magnitude of geopotential-height changes varies signif- icantly with location. The geopotential-height dataset is also model based, and may be adversely affected by the limited availability of ground-based observations in the tropics. However, the NCEP re-analyses dataset also incorporates TIROS (Television Infra-Red Observation Satellite) Operational Vertical Sounder (TOVS) retrievals, providing obser- vations to complement the ground-based data and allowing some confidence in the data across the tropical region.

In addition, this regression method cannot fully reproduce the geopotential-height- OLR relationship. Regression calculations are carried out for each block in isolation. Geopotential-height anomalies away from the equator forced by changes in equa- torial convection (and hence OLR) do not influence the fit. Similarly, Kelvin-wave geopotential-height anomalies are longitudinally displaced from the (low-OLR) region of forcing (Gill 1980) and are not fitted here.

Variability in the geopotential-height fields does appear to have a significant influ- ence on the local OLR, although from Fig. 7 it can be seen that the extent of the influence

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Figure 7. Percentage of outgoing long-wave radiation (OLR) variance explained by geopotential height at 20 mb (a) and at 200 mb (b). Calculated using the linear regression fit in Eq. (3).

shows much geographical variation. It is not clear from the calculations presented in this section whether the OLR fit to ozone is effective in modelling the same portion of the variability as that identified in fits with geopotential height. Further analysis is required, particularly at mid latitudes where the connection between geopotential height and ozone has been shown. However, the results do indicate that the relationship be- tween OLR and ozone could play an important role in improving the understanding of ozone variability, particularly in the context of the El Nifio oscillation.

6. THE EFFECTS O F OZONE ABSORPTION ON OUTGOING LONG-WAVE RADIATION

It is necessary to consider the possible direct impact on OLR of changes in the ozone profile. Changes in the ozone distribution (such as those related to the QBO) will change the OLR by affecting the rate of absorption by ozone in the stratosphere. It is, therefore, necessary to determine whether this effect is sufficiently strong to account for the apparent ozone-OLR relationship identified.

TROPICAL, TOTAL OZONE 1001

Experiments to determine the magnitude of this direct effect were carried out using an off-line radiation scheme (described more fully in Williams and Toumi (1999)) with ozone changes imposed such as to simulate the ozone QBO signal. The long- wave section of the scheme is adapted from that used in the European Centre for Medium-Range Weather Forecasts (ECMWF) model (Morcrette 1989). The ECMWF long-wave radiation scheme includes continuum absorption and all vibration-rotation bands for water (0-2820 cm-I), the 15 p m band of carbon dioxide (C02) and the 9.6 p m band of ozone. It also calculates the minor bands of C02 in the 800-1 200 cm- region, but ignores the minor trace gases and other bands of ozone. To allow a more accurate calculation of the overlapping effects of more radiatively active minor trace gases such as methane, nitrous oxide and chlorofluorocarbons, this scheme uses eight spectral intervals, in comparison with the six used in the ECMWF model. Four of the ECMWF intervals are retained. The other two, spanning regions where many of the absorption bands of minor trace gases are concentrated, are split to improve the treatment of these constituents. Long-wave fluxes are evaluated using a band emissivity method adapted from the Malkmus narrow-band model. Flux calculations are carried out for 43 levels in the long-wave scheme, with the model extending from 0 to 102 km. Resolution decreases with height such that, in the low troposphere, levels are separated by less than 1 km, in the upper troposphere and lower stratosphere the resolution is approximately 1.2 km. In the upper stratosphere and mesosphere there is reduced resolution, approaching 4 km between levels.

In order to assess the possible impact of ozone changes on OLR, a changed ozone profile is imposed and the difference in OLR determined. The ozone profile change applied was such that stratospheric ozone in the westerly wind regime below 28 km was decreased, with an increase above (easterly). The maximum changes (0.4 parts per million by volume (p.p.m.v.)> are applied in the layers adjacent to this 28 km change- over level, with smaller (0.2 p.p.m.v.) changes applied elsewhere. No change is applied below 20 km or above 77 km. These estimates of ozone change are based on SAGE I1 observations of the QBO ozone anomaly observed for January 1986 (Hasebe 1994). Values for 1986 were chosen from the available data (1985-89) as they exhibited the strongest and most clearly defined regimes of increase and decrease. As such, this rep- resents an extreme case for QBO ozone profile change. The change in OLR when this simulated ozone QBO effect was incorporated in the radiation scheme was found to be less than 0.03 W mP2.

Additional experiments were carried out to ensure that the small magnitude of the OLR changes identified was not a result of the profile shape of the ozone changes imposed. The effect of a uniform 10% increase in the ozone profile was investigated. Even in this very extreme scenario, the derived reduction in OLR was very small (less than 0.1 W m-*, about 0.05%).

7. CONCLUSION

The use of OLR in ozone regression analyses in place of SOI, after accounting for solar and QBO signals, is found to significantly improve the fitted ozone. Observed tropical-mean OLR and observed column ozone from the TOMS instrument are found to be positively correlated, such that increases in the OLR coincide with increases in the ozone column. For this to occur entirely as a result of a satellite sampling error, the climatology used to correct for the presence of cloud would need to systematically underestimate tropospheric ozone values. OLR was found to account for about 7.5% of the tropical-mean ozone variance, compared with the 4% explained by SOL The results

1002 V. WILLIAMS and R. TOUMI

shown suggest the possibility that OLR could provide a better proxy for ozone behaviour related to El Niiio than the SO1 which has previously been used in regression fit methods (e.g. Randel and Cobb 1994).

Using EOF analysis, it was shown that the variability in ozone associated with ENSO exhibited complex spatial behaviour, with several components of the ozone variability showing an apparent link with El Niiio. This further suggests that the use of any single time index, such as SOI, to model ENSO in ozone regression fit models could not adequately fit the related ozone change. In addition, it was shown that the de- seasonalization method, commonly used prior to performing such regression fits, fails to adequately remove all elements of the seasonal cycle from the data and may influence the spatial characteristics of the remaining data.

The ‘direct’ relationship between ozone and OLR arising as a result of the absorption of long-wave radiation by ozone is shown to be insufficient to explain the results obtained. This ‘direct’ effect is small in magnitude and is in the opposite sense to the relationship identified by the regression calculations. From the regression fit and EOF analyses, increases in ozone are found to coincide with increases, rather than decreases, in the observed OLR, for much of the year. A seasonal pattern in the relationship is observed, such that the magnitude of the ozone change related to a 1 W rn-* change in OLR is found to be largest in northern-hemisphere summer.

It is suggested that this positive correlation between ozone and OLR occurs as a result of the relationship between observed OLR and changes in the circulation in response to ENSO. The use of OLR data in ozone regression calculations would appear to provide a long-term, accurate dataset, capable of reflecting localized changes more effectively than the use of a single globally applied index such as SOT. Further investigation of the OLR-ozone link, with reference to the ozone-geopotential-height link already identified, should be carried out for mid-latitude observations. OLR is here shown to contribute to the understanding of observed ozone variability in the tropics, particularly during El Niiio. It is likely that the relationship would also be of use in the mid latitudes where it could be used as a proxy for geopotential height.

ACKNOWLEDGEMENTS

This work was supported by Natural Environment Research Council. Helpful dis- cussions with Joanna Haigh and Richard Bantges are appreciated. We also thank the referees for comments which improved the paper.

Angell, J. K.

Chandra, S., Ziemke, J. R., Min, W.

Collimore, C. C., Hitchman, M. H.

Dessler, A. E., Minschwaner, K.,

and Read, W. G.

and Martin, D. W.

Weinstock, E. M., Hintsa, E. J., Anderson, 3. G . and Russell 111, J. M.

Larsen, J. C. and Logan, J. A. Fishman, J., Watson, C. E.,

Gill, A. E.

REFERENCES

1997 Estimated impact of Agung, El Chichbn and Pinatubo volcanic eruptions on global and regional total ozone after adjustment for the QBO. Geophys. Rex Lett., 24,647-650

Effects of 1997-1998 El Niiio on tropospheric ozone and water vapor. Gevphys. Res. Lett., 25,3867-3870

Is there a quasi-biennial oscillation in tropical deep convection? Gevphys. Res Lptt., 3,333-336

The effects of tropical cirrus clouds on the abundance of lower stratospheric ozone. J. Amos. Chem., 23, 209-220

1998

1998

1996

1990

1980

The distribution of tropospheric ozone determined from satellite

Some simple solutions for heat induced tropical circulation. data. J. Geophys. Res., 95,3599-3617

Q. J. R. Metevrvl. SOC., 106,447463

TROPICAL TOTAL OZONE 1003

Haigh, J . D.

Hasebe, F.

Hood, L. L.

Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Leetmaa, A., Reynolds, R., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Jenne, R. and Joseph, D.

Kent, G. S., Williams, E. R., Wang, P. -H., McCormick, M. P. and Skeens, K. M.

Liehmann, B. and Smith, C. A.

McPeters, R. D., Krueger, A. J., Bhartia, P. K. and Herman, J. R.

Morcrette, J.-J.

Pawson, S. and Fiorino, M.

Randel, W. J. and Cobb, J. B.

Shiotani, M.

Stolarski, R. S . , Bloomfield, P., McPeters, R. D. and Herman, J . R.

Thompson, A. M., McNamara, D. P., Pickering, K. E. and McPeters, R. D.

Van Loon, H. and Labitzke, K.

Williams, V. and Toumi, R.

Zerefos, C. S., Bais, A. F., Ziomas, 1. C. and Bojkov, R. D.

Bais, A. F.

McPeters, R. D. and Newman. P. A.

Zerefos, C. S.. Tourpali, K. and

Ziemke, J. R., Chandra, S.,

1984 Radiative heating in the lower stratosphere and the distribution of ozone in a two-dimensional model. Q. J. R. Mefeornl. Soc.,

Quasi-biennial oscillations of ozone and diabatic circulation in the equatorial stratosphere. J. Atmos. Sci., 51, 729-745

The solar cycle variation of total ozone: Dynamical forcing in the lower stratosphere. J. Geophys. R e x , 102, 1355-1370

The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol.

110, 167-185 1994

I997

1996 SOL,. , 77, 43747 I

1995 Surface temperature related variations in tropical cirrus cloud as measured by SAGE 11. J. Climate, 8,2577-2594

1996

I996

Description of a complete (interpolated) outgoing longwave radi- ation dataset. Bull. Am. Meteorol. Soc., 77, 1275-1277

‘Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) data product users guide’. NASA RP-1384

1989 ‘Description of the radiation scheme in the ECMWF model’. Tech. Memo. 165, European Centre for Medium-Range Weather Forecasts, Reading, UK

A comparison of reanalyses in the tropical stratosphere. Part2: The quasi biennial oscillation. Clim. Dyn., 14,645458

Coherent variations of monthly mean total ozone and lower strato- spheric temperature. J. Geophys. Res., 99,5433-5447

Annual, quasi-biennial and El Nifio-Southern Oscillation (ENSO) time-scale variations in equatorial total ozone. J . Geophys. Res., 97,7625-7633

Total ozone trends deduced from Nimbus 7 TOMS data. Geophys. Rex Lett., 18, 1015-1018

1998

1994

1992

1991

1993 Effect ofmarine stratocumulus on TOMS ozone. J. Geophys. Rex, 98.2305 1-23057

1998 The global range of the stratospheric decadal wave. Part I: Its as- sociation with the sunspot cycle in summer, and in the annual mean, and with the troposphere. J. Climate, 11, 1529-1537

The radiative-dynamical effects of high cloud on global ozone distribution. J. Atmos. Chem., 33, 1-22

On the relative importance of the Quasi-Biennial Oscillation and El Nifio/Southern Oscillation in the revised Dobson total ozone records. J. Geophys. Res., 97, 10135-10144

Further studies on possible volcanic signals to the ozone layer. J. Geophys. Rex , 99,25741-25746

Dynamical proxies of column ozone with applications to global trend models. J. Geophys. Res., 102, 6 1 17-6 I 29

1999

1992

1994

1997