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Investigation on the possibilities of trend detection of spectral irradiance

Investigation on the possibilities of trend detection of spectral irradiance. Merle Glandorf and Gunther Seckmeyer Institute of Meteorology and Climatology University of Hannover. Investigation on the possibilities of trend detection of spectral UV irradiance. 1. Introduction

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Investigation on the possibilities of trend detection of spectral irradiance

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  1. Investigation on the possibilitiesof trend detectionof spectral irradiance Merle Glandorf and Gunther Seckmeyer Institute of Meteorology and Climatology University of Hannover

  2. Investigation on the possibilities of trend detection of spectral UV irradiance 1. Introduction 2. Trend detection for Thessaloniki and Sodankylä 3. How many years of data are needed to detect a trend? 4. Optimal wavelength for trend detection 5. Summary / Conclusions

  3. 1. Introduction • Over the past decades a worldwide depletion of ozone has been observed. • The inverse relationship between changes in total ozone and associated changes in solar UV irradiance is well documented • Up to the beginning of the project no significant trend in UV irradiance could be observed for Europe

  4. State of the art – some statements • Annually averaged erythemal irradiance [...] increased by about 6-14 % over the last 20 years at several mid- to high-latitude sites. • There is clear evidence that the long-term UV changes are not driven by ozone alone, but also by changes in cloudiness, aerosols, and surface albedo. • UV increases associated with the ozone decline have been observed by spectral measurements at a number of sites located in Europe. (from WMO Ozone Assessment, 2002)

  5. 2. Trend detection • Data from Thessaloniki and Sodankylä • Longest time series, both comprise > 11 years • Both recorded by single monochromator Brewer instruments • Data was retrieved from the EDUCE database via webpage interface (http://ozone2.fmi.fi/uvdb)

  6. Data selection Extraction of subsets with a constant combination of wavelength and solar zenith angle (SZA) • Wavelength range: 300-310 nm • Solar zenith angle range: Thessaloniki: 30°-65° Sodankylä: 44°-65°  data are comparable because effect of different solar zenith angles is removed

  7. Thessaloniki, 302 nm, 50° SZA

  8. 3. Trend analysis Trend analysis: • Linear regression analysis (method is justified by a residual analysis) • Calculation of significance of the detected increase or decrease • Data points are not equidistant! Most of the typical statistical methods require constant distances between data points  only few methods are applicable

  9. Spectral irradiance at a constant wavelength  Regression lines vary from one solar zenith angle to another

  10. Spectral irradiance at a constant SZA  Regression lines vary from one wavelength to the next

  11. Percentage change of irradiance per decade (Thessaloniki) Each symbol represents a solar zenith angle (30°-65°)

  12. Percentage change of irradiance per decade (Sodankylä) Each symbol represents a solar zenith angle (44°-65°)

  13. Results: • An average over all SZAs at one wavelength would have a positive sign • The variability of the calculated changes decreases towards higher wavelengths

  14. Factors affecting UV radiation- possible reasons for a change • SZA • Ozone • Clouds • Aerosols • Albedo

  15. Factors affecting UV radiation- possible reasons for a change • SZA • Ozone • Clouds • Aerosols • Albedo From WMO Ozone Assessment (2002)

  16. Results trend analysis • Ozone time series lacks a clear decrease in the analysed time interval  It is questionable if there could have been an ozone induced UV increase in the analysed time interval • Trend lines vary irregularly from one wavelength to the next and from one SZA to another  No unambiguous trend in Thessaloniki and Sodankylä data (as expected)

  17. 3. How many years of data are needed to detect a trend? • No unambiguous trend could be detected in the 11 years time series  how many years are needed? • Studies exist, e. g. from Weatherhead et al. (JGR, 1998) and from Lubin and Jensen (Nature, 1995) • Their methods couldn’t be applied to our data as they don’t meet the requirements for these methods (equidistance)

  18. How many years of data are needed to detect a trend? Our approach: • Detrending the original 11 years time series • An artificial trend is superimposed on the data which represents a UV increase due to a realistic ozone decrease (modelled with libRadtran) • Linear regression analysis • A time series is considered to be long enough if the calculated UV upward gradient caused by the given ozone loss is significant at the 99 % level • Significance > 99 %  time series is long enough Significance < 99 %  extension of time series

  19. How many years of data are needed to detect a trend? Our approach: • Detrending the original 11 years time series • An artificial trend is superimposed on the data which represents a UV increase due to a realistic ozone decrease (modelled with libRadtran) • Linear regression analysis • A time series is considered to be long enough if the calculated UV upward gradient caused by the given ozone loss is significant at the 99 % level • Significance > 99 %  time series is long enough Significance < 99 %  extension of time series

  20. How many years of data are needed to detect a trend? Our approach: • Detrending the original 11 years time series • An artificial trend is superimposed on the data which represents a UV increase due to a realistic ozone decrease (modelled with libRadtran) • Linear regression analysis • A time series is considered to be long enough if the calculated UV upward gradient caused by the given ozone loss is significant at the 99 % level • Significance > 99 %  time series is long enough Significance < 99 %  extension of time series

  21. How many years of data are needed to detect a trend? Our approach: • Detrending the original 11 years time series • An artificial trend is superimposed on the data which represents a UV increase due to a realistic ozone decrease (modelled with libRadtran) • Linear regression analysis • A time series is considered to be long enough if the calculated UV upward gradient caused by the given ozone loss is significant at the 99 % level • Significance > 99 %  time series is long enough Significance < 99 %  extension of time series

  22. How many years of data are needed to detect a trend? Our approach: • Detrending the original 11 years time series • An artificial trend is superimposed on the data which represents a UV increase due to a realistic ozone decrease (modelled with libRadtran) • Linear regression analysis • A time series is considered to be long enough if the calculated UV upward gradient caused by the given ozone loss is significant at the 99 % level • Significance > 99 %  time series is long enough Significance < 99 %  extension of time series

  23. How many years of data are needed to detect a trend? Our approach: • Detrending the original 11 years time series • An artificial trend is superimposed on the data which represents a UV increase due to a realistic ozone decrease (modelled with libRadtran) • Linear regression analysis • A time series is considered to be long enough if the calculated UV upward gradient caused by the given ozone loss is significant at the 99 % level • Significance > 99 %  time series is long enough Significance < 99 %  extension of time series

  24. Extension of time series

  25. How many years of data are needed to detect a trend? Our approach: • Detrending the original 11 years time series • An artificial trend is superimposed on the data which represents a UV increase due to a realistic ozone decrease (modelled with libRadtran) • Linear regression analysis • A time series is considered to be long enough if the calculated UV upward gradient caused by the given ozone loss is significant at the 99 % level • Significance > 99 %  time series is long enough Significance < 99 %  extension of time series

  26. Extension of time series • Caution! It has to be noted that this method requires a constant ozone trend for the complete analysed period. It also regards the original time series to be representative for future conditions. • Therefore: Results have to be regarded carefully!

  27. Number of years required for trend detection Each point is an average of all analysed solar zenith angles

  28. Results • Thessaloniki: After 15 years a UV increase due to an ozone trend of -4.5 % per decade should be detectable • Sodankylä: After 11 years at the earliest a UV increase due to an ozone trend of -5.7 % per decade should be detectable • Sodankylä: If there would had been such an ozone trend in the analysed period it would have been recognised in the UV time series.

  29. 4. What is the optimal wavelength for trend detection? Model results: Wavelengths between 295 and 305 nm are appropriate for detection of UV changes due to a 3 % ozone change (Bernhard and Seckmeyer, JGR, 1999)

  30. Optimal wavelength for trend detection • Thessaloniki: All wavelengths within the analysed range are almost equally appropriate for trend detection • Sodankylä: Wavelengths near 300 nm are most appropriate

  31. Optimal wavelength for trend detection Why are short wavelengths for trend detection not as appropriate for Thessaloniki data as for Sodankylä data?  We supposed that at lower wavelengths the standard deviation of Thessaloniki data shows a stronger increase than of Sodankylä data

  32. Standard deviation of the original time series This assumption couldn’t be confirmed: The standard deviation of Thessaloniki data doesn’t show a stronger increase than of Sodankylä data

  33. This figure also agrees with Arola’s conclusion (EDUCE, 2002): “The amplitude of the long-term variability caused by ozone is much stronger in Sodankylä than in Thessaloniki, so longer time series is needed to detect any possible trend.”

  34. 5. Summary • No unambiguous trend detected in Thessaloniki and Sodankylä data • Thessaloniki: UV trend due to an ozone trend of -4.5 % per decade should be detectable after 15 years • Sodankylä: UV trend due to an ozone trend of -5.7 % per decade should be detectable after 11 years at the earliest • Thessaloniki: in wavelength range 300-310 nm all wavelengths are almost equally appropriate for trend detection • Sodankylä: wavelengths near 300 nm are most appropriate for trend detection

  35. Conclusions • Longer time series are needed to detect any possible trend in UV • Implementation of flagging into the database can enhance the quality of all analyses • It would be desirable to study spectral global irradiance at wavelengths below 300 nm. Therefore also long UV time series from instruments with a good stray light rejection are needed

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