An Investigation on high/low frequency ozone and UV irradiance variations

An investigation on high/low frequency ozone and UV irradiance variations

Casale G.R., A.M. Siani, S. Miano, D. Meloni, S. Palmieri

University of Rome "La Sapienza", Physics Dept., P.le A. Moro 2, 00185 Rome, Italy

J. Groebner

JRC - Environment Institute, 21100 Ispra (VA), Italy

A.Galliani

Ufficio Generale per la Meteorologia, ReSMA, Vigna di Valle, Italy

Abstract

Introduction

The analysis of time ozone components (high/low frequency) of the Italian stations of Rome (41.9°N, 12.5°E), Vigna di Valle (42.1°N, 12.2°E) and Ispra (45.8°N, 8.6°E), and the Swiss Arosa (46.8°N, 9.7°E) are investigated in order to single out any effective ozone trend together with the role of ozone fluctuations due to weather patterns.A filtering technique and an advanced statistical methodology are applied to the Dobson ozone long time series of Vigna di Valle and Arosa and to the Brewer ozone data of Rome and Ispra. A sensitivity study of the STAR model for the UV spectral and integrated irradiance in 2040 is performed using different assumptions.

Data

The Brewer daily total ozone measurements from 1992 up to 1999 are analysed to study the temporal behaviour at Rome and Ispra. The Dobson total ozone data are derived from the monthly long time series of Vigna di Valle (50km north of Rome) for the years 1958-1999 and Arosa (170km north-west of Ispra, Swiss Alps) for the time period 1927-1999. The Dobson data are extracted from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC).

Methodology and results for O₃ data

In order to better understand the temporal fluctuations of the atmospheric ozone content, the Rao and Zurbenko technique (RZ) is applied (Eskridge et al., 1997; Meghan et al., 1998) to the long ozone time series of Vigna di Valle and Arosa, and to the Italian Brewer data sets. The different time scales involved can be separated, allowing to identify the effective ozone trend.

Following the RZ method, the Brewer ozone time series can be splitted in a long-term component (time period greater than 2 years), a seasonal component (time period below 2 years but greater than 1 month), a short-term component (time period below 1 month, related to weather patterns, which we call high frequency ozone). The two remaining terms of turbulence and instrumental error may be considered negligible here, giving a contribute only when single daily ozone data are considered. From the analysis, it seems that meteorological factors affect Rome data more than Ispra’s (Casale et al., 2000). On the other hand, the seasonal term plays a significant role in explaining Ispra variability compared to Rome.The same RZ analysis can be applied to the monthly ozone time series of Vigna di Valle and Arosa. After filtering all cyclical variations smaller than the 11 years solar cycle, the decline is singled out starting from 1970s for Arosa (2.20%/10 years) and from 1980s for Vigna di Valle (4.98%/10 years).

In order to characterize the O₃ trend, a well-known advanced statistical methodology is applied to the long time series of yearly and monthly mean values (low frequency ozone) to ascertain the internal structure of the series. Therefore simple randomness of the series is tested against serial correlation and against trend. The trend analysis is used in a sequential way and completed by a change point search procedure. For this purpose, as suggested by Sneyers, Mann?Kendall-Sneyers trend tests (MKS) are used (Sneyers, 1990; Sneyers, 1992).

The tests are non parametric (distribution free) having optimal power relative to the corresponding parametric ones. The trend test is applied in a progressive sequential onward and backward way in order to avoid false conclusions due to the eventual existence of compensating internal trends. A Pettitt test (1979) completes the analysis showing the possible abrupt character of climate instability. The change point is estimated in the case of series divided by one such point into two sub-series with or without trend. A significance level of 95% is chosen to interpret the results. Under this condition, the hypothesis of trend is accepted or rejected at the level of 5%, depending on whether probability is <5% or >5%.

Tables 1 and 2 show the MKS, Pettitt and RZ analysis’ results. The column "Zurbenko" in Table 2 indicates the slope of regression line of the long term component obtained by using the RZ technique, cross-checking the validity of the trend. The trend slope is determined from around 1970 at Arosa and from around 1980 at Vigna di Valle (see column "year" in Table 2).

Table 1. Summary of the statistical tests on O₃ on yearly basis. The trend is expressed as %/10years

Station and data Instr. Period Mann-Kendall-Sneyers

sign trend prob

VV Yearly 1958-99 - 1.57 1.24

AR Yearly 1927-99 - 0.63 0.04

AR Yearly 1958-99 - 1.76 <0.01

VV: Vigna di Valle; AR: Arosa; prob: probability <5%

Table 2. Results of Pettitt’s test on O₃. The third column (Pettitt) indicates the "change point" year;

the last three columns report the trends before and after the change point and the RZ results

Station and data Instr. Period Pettitt
left trend

%/10 years

right trend

%/10 years
Zurbenko

trend year

VV Yearly 1958-99 1983 +0.22 -1.09 -4.98 1980

AR Yearly 1927-99 1978 <0.01 -3.20

AR Yearly 1958-99 1979 -0.48 -3.20 -2.20 1970

VV: Vigna di Valle; AR: Arosa

It is pointed out on yearly basis, by means of MKS methodology (Table 1), the existence of a negative trend of 0.63% every ten years, in the period 1927-1999, and 1.76%/10 years in the period 1958-99 at Arosa. A negative trend of 1.57% every ten years, from 1958 to 1999, is estimated at Vigna di Valle. By means of Pettitt test, the "change point" year is estimated (Table 2): around 1983 at Vigna di Valle and around 1978 at Arosa. A significant decreasing trend of ?1.09% every 10 years at the first site and ?3.2%/10 years at the second one is detected from the change point on.

The same method of analysis applied to the yearly mean amount is extended on monthly basis. Table 3 and 4 present a summary of monthly ozone trends estimated in the period 1958-1999 (VV) and 1927-1999 (AR) considering respectively the whole record time period and that from the change point. The reported probability (<5%) refers solely to the significant negative trends

Table 3. Summary of the statistical tests on O₃ on monthly basis. The sign + indicates significant positive trend; the sign - indicates significant negative trend; a blank cell indicates no trend

Station Month Sign
Trend

(%/10 years)

Probability

(%)

VV

(1958-1999)
Jan Feb Mar - - 1.50 2.86 0.41 1.08

Apr May Jun - 1.52 2.26

Jul Aug Sep

Oct Nov Dec - - 1.52 2.71 0.36 0.005

AR

(1927-1999)
Jan Feb Mar - - - 1.23 0.89 1.21 0.14 4.44 0.09

Apr May Jun - - 0.73 0.89 2.26 0.12

Jul Aug Sep - 0.22 2.78

Oct Nov Dec - - - 0.46 0.70 1.08 1.55 0.09 0.003

Table 4. Summary of the statistical tests on monthly basis on O₃ at the right of the change point. The sign + indicates significant positive trend; the sign - indicates significant negative trend; a blank cell indicates no trend

Station Month Sign
Trend

(%/10 years)

Probability

(%)

VV

(1958-1999)

c.p. 1983
Jan Feb Mar

Apr May Jun - 4.42 1.73

Jul Aug Sep - 1.90 2.78

Oct Nov Dec

AR

(1958-1998)

c.p. 1979
Jan Feb Mar - - 6.82 5.93 3.32 1.60

Apr May Jun - 4.63 2.32

Jul Aug Sep

Oct Nov Dec

c.p.: change point

These results are in accordance with recent studies on ozone decline by Bojkov and Fioletov (1995) Bojkov et al. (1998) and the last WMO Scientific Assessment of Ozone Depletion (1999).

Temperature trends and UV in 2040 at Ispra and Rome

To study the possible effect of atmospheric changes on UV radiation (UV irradiance variations), the same methodology applied to the ozone series is used to analyse the surface temperature trends at Rome and Ispra. The analysis on the time series of monthly values in the period 1958-1998 at Ispra and 1926-1997 at Rome shows the existence of trends only if the detected change points are not considered.

The effect of the observed cooling of the stratosphere is considered referring to the latest published trends (WMO, 1999) to the levels between 100hPa and 1hPa.

The STAR model (Ruggaber et al., 1994) is used to derive the 305 nm UV irradiance, the integrated 290-325 nm irradiance and the erythemally weighted irradiance in the year 2040 under five different hypotesis:

1A: ozone decrease following a linear extrapolation from year 2000 to 2040, based on Table 3 results;

1B: ozone decrease as described in 1A coupled with the surface monthly temperature increase linearly extrapolated;

2A: ozone decrease following a linear extrapolation from year 2000 to 2040, based on Table 4 results;

2B: ozone decrease as described in 2A and surface temperature increase as in 1B;

2C: the same as in 2B coupled with the stratospheric temperature decrease as reported in the last ozone assessment (WMO, 1999).

Results for 1A-1B and 2A-2B-2C hypothesis are reported in Table 5 and Table 6 respectively. The uncertainties of the model (17% - Schwander et al., 1997) allow to detect only the 305 nm irradiance increases for 1A and 1B. Hypotesis 2A, 2B and 2C lead to a more significant increase in the integrated and erythemally weighted irradiance and the results are shown in Table 7 for 2C case. The significance level is there lowered to 14%.

Table 5. Mean monthly percentage differences of O₃ and UV radiation at 305 nm between 1999 and 2040 due to a linear decrease in ozone. Calculations are performed with and without surface temperature forcing (1A and 1B hypothesis, see text). No significant differences exist in the output. Blank cells indicate absence of trend; significant trends (>17%, as obtained from Schwander et al., 1997) are in bold.

ROME

ISPRA

Month
(%)

Irradiance

305 nm

(%)

Ozone

(%)

Irradiance

305 nm

(%)

Ozone

Jan +21.3 -6.0 +16.4 -4.9

Feb +39.1 -11.4 +9.0 -3.6

Mar +10.3 -4.8

Apr +3.9 -2.9

May +11.1 -6.1 +4.6 -3.6

June

July

Aug

Sept

Oct +1.3 -1.8

Nov +17.0 -6.1 +6.0 -2.8

Dec +40.5 -10.8 +13.6 -4.3

Table 6. Mean monthly percentage differences in 305 nm UV and ozone without and with temperature forcing. Change points are included as in conditions 2A, 2B and 2C (see text). Blank cells indicate absence of trend; significant trends (>17%, as obtained from Schwander et al., 1997) are in bold.

ROME

ISPRA

Month
(%)

Irradiance

305 nm

(%)

Ozone

(%)

Surface Temp

(%)

Irradiance

305 nm

(%)

Ozone

(%)

Surface Temp

2A 2B 2C 2A 2B 2C

Jan -1.1 +1.8 +1.8 -3.3 -0.2 +18.2

Feb -1.1 +1.3 +1.2 +141 +141 +145 -27.3

Mar +0.9 +85.5 +85.5 +88.3 -23.7

Apr +0.6 -0.9

May -1.0 +0.6 +0.4 +42.5 +42.5 +44.5 -18.5

June +35.5 +35.5 +37.2 -17.7 -1.0

July -1.0 +0.6 +0.4 -2.8 -1.0 +1.9

Aug +12.9 +12.9 +14.6 -7.6 +0.4 -2.9 -1.2 +2.8

Sept +0.7 -1.3

Oct +0.9 -1.3

Nov +1.3 -0.3

Dec +1.8 -3.0 <0.01 +10.4

Table 7. Mean monthly percentage differences in integrated not weighted UV and integrated erythemally weighted UV after running under 2C hypothesis (see text). Ozone decrease is evaluated at the right of the change point. Blank cells indicate absence of trend; significant trends (>14%, as obtained from Schwander et al., 1997) are in bold.

ROME

ISPRA

month
%

integrated irradiance
% Ozone
%

Surface

Temp

%

integrated irradiance
% Ozone
%

Surface

Temp

Not weighted
Erythem.

Weighted
Not weighted
Erythem.

Weighted

Jan -0.3 +0.2 +1.8 -2.8 -2.1 +18.2

Feb -0.4 +0.1 +1.2 +17.1 +56.3 -27.3

Mar -0.5 +0.0 +12.1 +44.0 -23.7

Apr -0.5 -0.0 -2.7 -1.7

May -0.5 +0.1 +0.4 +7.7 +30.8 -18.5

June +8.6 +30.5 -17.7 -2.4 -1.6

July -0.4 +0.2 +0.4 -2.4 -1.6 +1.9

Aug +3.3 +11.7 -7.6 +0.4 -2.5 -1.7 +2.8

Sept -0.5 +0.1 -2.8 -2.0

Oct -0.5 +0.1 -3.0 -2.3

Nov -0.4 +0.1 -3.0 -2.4

Dec -0.4 +0.3 -2.7 -2.0 +10.4

Conclusions

Results may be summarised as follows:

ozone temporal fluctuations can be separated into a high frequency component, weather related, and a low frequency component, affecting trends;
yearly mean ozone series show a decreasing trend (Table 1) with change points in 1983 (Vigna di Valle) and 1978 (Arosa) (Table 2);
a negative ozone tendency is detected on a monthly basis, prevailing in winter/spring (Table 3 and Table 4);
five different hypothesis (named 1A, 1B, 2A, 2B, 2C) are tested by means of STAR model in order to evaluate the changing UV radiation in 2040 with respect to 1999. Effects on 305 nm irradiance are evident in all conditions while integrated irradiance increases are significant when smaller ozone amounts are used as input (Table 5, 6 and 7).

References

Bojkov, R.D and V.E. Fioletov: "Estimating the global ozone characteristics during the last 30 years" J. Geophys. Res.100, D8, 16537-16551 (1995).

Bojkov, R.D., D.S. Balis and C.S. Zerefos: "Characteristics of the ozone decline in the Northern Polar and Middle Latitudes during the Winter- Spring" Metorol. Atmos. Phys. 69, 119-135 (1998).

Casale, G.R., D. Meloni, S. Miano, A.M. Siani, S. Palmieri, F. Cappellani: "Solar UV-B irradiance and total ozone in Italy: fluctuations and trends" J. Geophys. Res. 105, 4895-4901 (2000).

Eskridge, R.E., J.Y. Ku, S.T. Rao, P.S. Porter, I.G. Zurbenko: "Separating different scales of motion in time series of meteorological variables", Bull.Amer. Meteor. Soc.78 (7), 1473-1483 (1997).

Meghan, L. M., S.T. Rao, and I.G. Zurbenko: "Evaluating the Effectiveness of Ozone Management Effort in the Presence of Meteorological Variability" Jour. of the Air & Waste Management Assoc. 48, 201-1215 (1998).

Pettitt, A.N.: "A non parametric approach to the change-point problem" Applied statistics 28, 2, 126-135 (1979).

Ruggaber, A., R. Dlugi, T. Nakajima: "Modelling radiation quantities and photolysis frequencies in the troposphere" J. Atmos. Chem. 18, 171-210 (1994).

Schwander, H., P Koepke, A. Ruggaber: "Uncertainties in modelled UV irradiances due to limited accuracy and availability of input data" J. Geophys. Res. 102, 9419-9429 (1997).

Sneyers, R.: "On the statistical analysis of series of observations" WMO Technical Note n.143 (1990).

Sneyers, R.: "On the use of statistical analysis for the objective determination of climate change" Meteorol.Zeitschrift 1, 247-256 (1992).

WMO: "Scientific Assessment of Ozone Depletion: 1998", Rep. No.44 (1999).

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Session 1 : Stratospheric Processes and their Role in Climate Session 2 : Stratospheric Indicators of Climate Change

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Station and data		Instr. Period	Mann-Kendall-Sneyers
Station and data		Instr. Period	sign	trend	prob
VV	Yearly	1958-99	-	1.57	1.24
AR	Yearly	1927-99	-	0.63	0.04
AR	Yearly	1958-99	-	1.76	<0.01

Station and data		Instr. Period	Pettitt	left trend %/10 years	right trend %/10 years	Zurbenko
						trend		year
VV	Yearly	1958-99	1983	+0.22	-1.09	-4.98	1980
AR	Yearly	1927-99	1978	<0.01	-3.20
AR	Yearly	1958-99	1979	-0.48	-3.20	-2.20	1970

*Station*	*Month*			*Sign*			*Trend* (%/10 years)			*Probability* (%)
VV (1958-1999)	Jan	Feb	Mar	-	-		1.50	2.86		0.41	1.08
	Apr	May	Jun		-			1.52			2.26
	Jul	Aug	Sep
	Oct	Nov	Dec		-	-		1.52	2.71		0.36	0.005
AR (1927-1999)	Jan	Feb	Mar	-	-	-	1.23	0.89	1.21	0.14	4.44	0.09
	Apr	May	Jun	-	-		0.73	0.89		2.26	0.12
	Jul	Aug	Sep	-			0.22			2.78
	Oct	Nov	Dec	-	-	-	0.46	0.70	1.08	1.55	0.09	0.003

	ROME		ISPRA
Month	(%) Irradiance 305 nm	(%) Ozone	(%) Irradiance 305 nm	(%) Ozone
Jan	+21.3	-6.0	+16.4	-4.9
Feb	+39.1	-11.4	+9.0	-3.6
Mar			+10.3	-4.8
Apr			+3.9	-2.9
May	+11.1	-6.1	+4.6	-3.6
June
July
Aug
Sept
Oct			+1.3	-1.8
Nov	+17.0	-6.1	+6.0	-2.8
Dec	+40.5	-10.8	+13.6	-4.3

Session 1 : Stratospheric Processes and their Role in Climate	Session 2 : Stratospheric Indicators of Climate Change
Session 3 : Modelling and Diagnosis of Stratospheric Effects on Climate	Session 4 : UV Observations and Modelling
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