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EDUCE IGF PAS PARTICIPATION

EDUCE IGF PAS PARTICIPATION

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EDUCE IGF PAS PARTICIPATION

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  1. EDUCE IGF PAS PARTICIPATION J.W. Krzyścin, J. Borkowski, J. Jarosławski, and P. Sobolewski Institute of Geophysics, Polish Academy of Sciences, 01-452 Warsaw, Poland WPX1 ESTIMATE OF THE NATURAL LEVEL AND VARIABILITY OF SURFACE UVR OVER POLAND

  2. Retrieval of Aerosol Optical Depth from the UV Direct Sun Intensity Measured by the Brewer during total ozone calculation procedure The method uses the Langley plot estimation of extraterestrial constants article inpress J.Geophys.Res.

  3. Ǻngström Formula: Aerosol Optical Depth = Visible rangeα = 1.3  0.6 ǺngströmExponent α versusAOD at 320 nm for selected pair of wavelength α < 0 ! →

  4. Comparison of the surface irradiance and actinic flux • at 310 nm by LibRadtran model for various input: • full input (total ozone-Brewer, ozone profile-Dobson Umkehr, • temperature-rawind sonde, AOD-Brewer, SSA+ phase function-CIMEL, • classic input (total ozone-Brewer, standard profiles for NH midlatitudes, • AOD at 550 nm-CIMEL, Ǻngström Exponent=1.3, rural aerosol model) Biomass burning ←

  5. Biometr Irradiances (SL 501A)for the period 1993-2002 0 Belsk SZA=60

  6. Ozone Vertical Profiles by Umkehr Observations

  7. Partial Radiation Amplification Factors (RAF), , for ozone profile impact on UVand Radiation Amplification Factor due to aerosol, Modelapplied for broadband irradiances taken at SZA=60 and 80 by SL501 A over Belsk for the cloudlessperiod 1993-2002 Partial RAFs depend on SZA UV more sensitive to ozone content in upper levels for large SZA RAF due to aerosols almost constant

  8. Our Findings • Brewer Spectrophotometer – instrument providing spectral aerosol optical depth • No long-term aerosol forcing on the surface UV at Belsk • Aerosol properties in the UV range can not be extrapolated from those in the visible range • Partial Radiation Amplification Factor (RAF) due to ozone changes in selected Umkehr layers depend on SZA - empirical evidences for the ozone profile impact on UV. • Significant impact of day-to-day changes in aerosols on UV irradiance – RAF due to aerosols ~0.12 (weak dependence on SZA)

  9. EDUCE IGF PAS PARTICIPATION WPX1 EXAMINATION OF SURFACE VARIABILITY AT DIFFERENT TIME SCALES IN SURFACE UVR METHOD FOR RECONSTRUCTION OF UVR TIME SERIES AT EUROPEAN SITES

  10. MULTIRESOLUTIONDECOMPOSITION OF THE DATA SERIES USING WAVELETS. The wavelet expansion of a time series f(t) enables us to perform a multiresolution decomposition, which separates the series into components: f(t)=SJ(t)+DJ(t)+DJ-1(t)+…….. D1(t). where functions SJand Dj(t)are called the smooth and detail components respectively. The advantage of wavelet methods is that wavelets provide exact scale-based decomposition results, and can be applied for nonstationary processes.

  11. Our Findings: • Components of the wavelet multiresolution decomposition represent variations at time scales from 2 to 64 months and „smooth” variations • UV responds differently to ozone and cloudiness variations at different time scales • Modeling different components separately results in good agreement between modeled and observed values particularly for longer time scales • The smooth component can be perfectly reconstructed with ozone and cloudiness as explanatory variablesonly

  12. Annales Geophysicae, 2003, 21, 1887-1896Non-linear (MARS) modelling of long-term variations of surface UV-B radiation:As revealed from the analysis of Belsk, Poland, data for the period 1976-2000 Regressors: Belsk’sDobson total ozone, Cloud cover from NCEP/NOAA Reanalysis, Teleconnection Indices (QBO+NAO+ENSO). 11-year solar cycle Regressors: Belsk’s Dobson total ozone Total solar radiation from Belsk’s pyranometer Teleconnection Indices (QBO+NAO+ENSO) 11-year solar cycle ↕ ↕

  13. Comparison the model-observation simulation of the interannual changes in the UV data BELSK: Monthly means for May-October subperiods in 1976-2000 The simplest model The most sophisticated model  

  14. Our Findings • Nonlinear Regression Model-MARS resolves larger part of month-to-month variations but small improvement of the long-term fit to the observed UV radiation • Long-term Cloud Effects from Reanalysis-1 Data Base – UV reconstruction for any site (if ozone data available) • NAO and SOI effects on UV by MARS model (impact on atmospheric optical depth over Belsk)

  15. EDUCE IGF PAS PARTICIPATION WPX1 Verification and Extension of UV Climatology at Selected European Sites

  16. Objectives • Calibration • UV Doses Climatology • Long-term Changes of UV dose • Clouds and Total Ozone Effects • on UV Doses long-term changes Toravere Belsk Hradec K. Results based on the article submitted to Journal Geophysical Research

  17. Model Verification CRF –Cloud Reduction Factor IGF PAS model: RIVMmodel: • Gl - measured daily sum of total solar radiation • Gl*- modelled clear-sky sum of total solar radiation 2

  18. UV-Climatology

  19. Long-term UV variations from the reconstructed UV dataHradec Kralove 1964-2001 (April-September) Monthly UV doses Monthly UV doses cloud effects UV daily doses –clear skies Monthly UV doses – ozone effects

  20. OurFindings: • Reconstruction of the UV time series from historical time series of total ozone and global solar radiation • Delineation and correction method for the instrument drift in historical time series of global solar and UV radiation • Establishing the UVR climatology for Central Europe (seasonal profile of UVR and the clouds forcing on UVR ) • Estimations of the clouds and total ozone long-term effects on the surface UVRsince mid 1960s

  21. EDUCE IGF PAS PARTICIPATION WPX2 Estimation of trend variability in Belsk at least 2 other sets

  22. TREND DETERMINATION I. There is no precise, commonly accepted definition of a trend. II. Pristley [1981] refers to trend as “…tendency to increase (or decrease) steadily over time” Kendall [1976] -“the essential idea of trend is that it shall be smooth”. III. Most popular approach y(t)=C*t +e(t) C depends on time period considered, and does not show variations of the trend within the time period

  23. WAVELET APPROACH The „smooth” component of the signal is extracted with the use of wavelet multiresolution decomposition 2. Trend is defined as time derivative of the smooth component of the signal.

  24. Conclusions: • Trend is function of time • Trend determined from smooth component is not contaminated by fluctuations with short time scale • The problem of the influence of the kind of wavelets on trend is beeing investigated

  25. EDUCE IGF PAS PARTICIPATION WPX3 Submission of spectra and broad-band measurements The data sets from Belsk in the UV data base: The 1993-2003 UV spectra from the Brewer spectrophotometer ( ~ 86000 spectra) Erythemal irradiances by SL 501A biometer (resolution 5 minut) – 1993-2003 Global Solar irradiances by Kipp@Zonen CM1 pyranometer(resolution 5-minut) – 1994-2003 Total ozone from the Dobson spectrophotometer (daily representatives 1993-2002)