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Climate Data Homogenization An overview

Climate Data Homogenization An overview. By Enric Aguilar* CCRG Geography Unit Universitat Rovira i Virgili de Tarragona Spain Presentation to the Workshop on Enhancing South West Asian Climate Change Monitoring and Indices October 2004.

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Climate Data Homogenization An overview

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  1. Climate Data HomogenizationAn overview By Enric Aguilar* CCRG Geography Unit Universitat Rovira i Virgili de Tarragona Spain Presentation to the Workshop on Enhancing South West Asian Climate Change Monitoring and Indices October 2004 * This presentation was prepared in part by Lucie Vincent, Climate Research Branch, Meteorological Service of Canada Environment Canada

  2. OVERVIEW 1) WHAT IS A HOMOGENEOUS TIME SERIES? 2) WHAT DOES HOMOGENIZATION IMPLY? 3) SOME EXAMPLES

  3. What is a Homogeneous Climate Time Series? “A homogeneous climate time series is defined as one where variations are caused only by variations in climate” (WMO-TD No. 1186)

  4. Example of Homogeneous & Inhomogeneous Climate Time Series Annunal Average of Monthly Maximum Temperatures. Madrid, Spain Artificial bias

  5. Why do we need homogeneous data? • climate monitoring • trend analyses • climate change studies

  6. What does Climate Data Homogenization involve?

  7. Importance of station history - metadata Metadata provides essential information for climate data homogenization • type of instruments • location & exposure • observer & observing time • measurements & observing practices State of history files • often old, bulky, incomplete, not updated • needs to be preserved or digitized Station history files

  8. Example of Climate Series Homogenization Annual mean minimum temperatures of Quebec City, 1895-2002 Best-fit linear trend of -0.7°C over 106 years

  9. Creation of reference series A reference series is obtained by computing a time series of a weighted average of the surrounding stations Surrounding stations should have similar climate characteristics Strong discontinuities in surrounding stations can interfere with the proper identification of the discontinuities in the tested station 7054095 La Pocatiere 1913-1995 7022160 Drummondville 1913-1995 7018564 Trois-Rivieres 1963-1995 7018000 Shawinigan 1950-1995 7074240 La Tuque 1911-1995 100 km

  10. Detection of change points Difference between Quebec City and a reference series A statistical technique is used for detecting change points in the difference between the tested series and reference series 1931 1942 1960

  11. Importance of station history - metadata Good metadata are needed to ensure that the final data user has no doubt about the conditions in which data have been recorded, gathered and transmitted, in order to extract accurate conclusions from their analysis. (WMO-TD No. 1186)

  12. Main causes of discontinuities All • relocations (different impact on different climatic elements) Temperature • changes in instruments exposure • changes in observing time Precipitation • changes in rain gauge: under catch due to wind, evaporation loss, retention on funnel • changes in measuring instruments: snow ruler versus Nipher Automation of climate observations • parallel observations are essential • distinction between liquid and solid precipitation Change in instruments exposure often cause discontinuities Parallel observations can be useful to determine the adjustments required on daily observations

  13. Searching station history files Difference between Quebec City and a reference series Location of the instruments on the roof of the main building 1942-1960 1931 1942 1960 1931: relocation of the instruments at the college with change in exposure 1942: station relocation from college to airport Location of the instruments on the ground after 1960

  14. Adjustment of climate data Monthly and daily adjustments for the step identified in 1960 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1931 1942 1960

  15. Impact of instruments relocation on the trendQuebec City, 1895-2002 Annual mean minimum temperatures Adjusted annual mean minimum temperatures Trend of -0.7°C over 106 years Trend of 2.1°C over 106 years

  16. Implications on analyses of extremes • Techniques developed & applied on annual & monthly data • Daily and hourly values presented new problems due to extremes • Extremes are rare events with unique weather conditions (small sample size) • Homogeneity adjustment for these unique conditions can be difficult • Visual inspection of the time series of the extremes can provide a reliable preliminary assessment of the homogeneity in the extremes

  17. Example: analysis of extreme cold days • Percent of days with daily maximum temperature < 10th percentile was obtained at 16 stations in the Caribbean region • Visual assessment of the time series of this index indicates that one station has serious discontinuities in daily maximum temperature • The index of this station should not be used in trend and regional analyses Peterson et al., 2002

  18. An example of the Impact of homogenization on Indices Analysis: TX10P • Madrid, Spain. Homogenized (SNHT+ Vincent et al., 2002) • Marid, Spain. Raw Data

  19. Thanks!

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