Homogenization of daily data series for extreme climate index calculation Lakatos, M., Szentimey T. Bihari, Z., Szalai, S. Meeting of COST-ES0601 (HOME)

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Presentation transcript:

Homogenization of daily data series for extreme climate index calculation Lakatos, M., Szentimey T. Bihari, Z., Szalai, S. Meeting of COST-ES0601 (HOME) and 6th Seminar for Homogenization and Quality Control in Climatological Databases, May 2008

Climate index calculation 1998 CCL (Comission for Climatology of WMO) / CLIVAR (Research program me on CLImate VARiability and predictability) ECA&D (European Climate Assessment and Dataset) NKFP-3A/082/2004 „ Regional Climate Modeling” 2005 – 2007 (National Office for Research and Technology) CECILIA (Central and Eastern Europe Climate Change Impact and Vulnerability Assesment) WP4: extreme index calculation on homogenized, gridded observations and on RCM’s data

Meeting of COST-ES0601 (HOME) and 6th Seminar for Homogenization and Quality Control in Climatological Databases, May 2008 Index/unitWarm extremes txx / °Cabsolute Tmax dtx25/daysummer days Tmax > 25 °C (SU) dtx30e/dayhot days Tmax >=30 °C dtx35e/dayvery hot days Tmax >= 35 °C dtn20/daytropical nights Tmin > 20 °C (TR) ditxgnr/dayheat wave duration index (HWDI) ditgnr90/daywarm spell days itxgnr90/day maximum duration of warm spell (TXHW90 in Climdex) dtgnr90/%Tavg > 90th percentile of normal period (TG90p) dtngnr90/%Tmin > 90th percentile of normal period (TN90p) dtxgnr90/dayTmax > 90th (TX90) Index/unitCold extremes tnn/°Cabsolute Tmin dtn0/daysfrost days Tmin < 0°C (FD) itn0x/days maximum number of frost days Tmin < 0°C (CFD) dtn0e/daysfrost days Tmin <= 0°C t17s/°Cheating degree days (HD17) t20s°Cheating degree days dtx0/dayice days Tmax < 0°C (ID) dtx0e/dayice days with Tmax <= 0°C ditnlnr/daycold wave duration index (CWDI) ditlnr10/daycold spell days (CWFI) itnlnr10/day maximum duration of cold spell TNCW10 Climdex) dtlnr10/day Tavg < 10th percentile of normal period (TG10p) dtnlnr10/day Tmin < 10th percentile of normal period (TN10p) dtxlnr10/day Tmax < 10th percentile of normal period (TX10p) Index/unitPrecipitation Indices rs/mmPrecipitation sum dr1/dayNumber of wet days r1a/mm/dayMean wet-day precipitation ir0xd/dayLength of longest very dry period R≤1 mm ir1xd/dayLength of longest dry period R <1 mm ir1xw/dayLength of longest wet period R ≥ 1 mm dr5/dayNumber of days R ≥ 5 mm dr10 dayNumber of days R≥ 10 dr20/ dayNumber of days R≥ 20 rx1/mmMaximum daily sum rx5/mmMaximum sum in 5 day long period dr75gnr/dayNumber of days R> 75% of normal period pdr75gnr/% Number of days R> 75% of normal period in % of wet days r75gnr/mmPrecipitation sum of days R> 75% of normal period ~95 % 99 %

Meeting of COST-ES0601 (HOME) and 6th Seminar for Homogenization and Quality Control in Climatological Databases, May 2008 What we have done? Original, complemented, quality controlled and homogenized temperature indices series and theirs trends for 15 stations and precipitation indices series for 58 stations , annual and seasonal scale and for ~10 km gridded data

Meeting of COST-ES0601 (HOME) and 6th Seminar for Homogenization and Quality Control in Climatological Databases, May 2008 Multiple Analysis of Series for Homogenization; Szentimrey MASH Climate indices calculations require at least daily resolution of homogeneous time series.

Meeting of COST-ES0601 (HOME) and 6th Seminar for Homogenization and Quality Control in Climatological Databases, May 2008 The new version MASHv3.02 (extended also for daily precipitation data) The algorithm for daily data homogenization is as follows: 1.Monthly values from daily data. 2.MASH homogenization procedure for monthly series, estimation of monthly inhomogeneities. 3.On the basis of estimated monthly inhomogeneities, continuous (smooth) estimation for daily inhomogeneities. 4.Homogenization of daily data. 5.Quality control for homogenized daily data. 6.Missing daily data complementing. 7.Monthly values from homogenized, controlled and complemented daily data. 8.Test of homogeneity for the new monthly series by MASH homogenization procedure. Repeating steps if it is necessary.

Meeting of COST-ES0601 (HOME) and 6th Seminar for Homogenization and Quality Control in Climatological Databases, May 2008 Verification of homogenization VERIFICATION OF HOMOGENIZATION (MINIMUM) TEST STATISTICS for ANNUAL SERIES (OUTPUT of MASH) Critical value (significance level 0.05): Test Statistics Before Homogenization(TSB) Station TSB Station TSB Station TSB AVERAGE: Test Statistics After Homogenization(TSA) Station TSA Station TSA Station TSA AVERAGE: 26.85

Meeting of COST-ES0601 (HOME) and 6th Seminar for Homogenization and Quality Control in Climatological Databases, May 2008 VERIFICATION OF HOMOGENIZATION (MAXIMUM) TEST STATISTICS for ANNUAL SERIES (OUTPUT of MASH) Critical value (significance level 0.05): Test Statistics Before Homogenization(TSB) Station TSB Station TSB Station TSB AVERAGE: Test Statistics After Homogenization(TSA) Station TSA Station TSA Station TSA AVERAGE: Verification of homogenization

Meeting of COST-ES0601 (HOME) and 6th Seminar for Homogenization and Quality Control in Climatological Databases, May 2008 Verification of homogenization VERIFICATION OF HOMOGENIZATION (PRECIPITATION) TEST STATISTICS for ANNUAL SERIES (OUTPUT of MASH) Critical value (significance level 0.05): Test Statistics Before Homogenization(TSB) Station TSB Station TSB Station TSB ……………… AVERAGE: Test Statistics After Homogenization(TSA) Station TSA Station TSA Station TSA ………………… AVERAGE: 29.13

Meeting of COST-ES0601 (HOME) and 6th Seminar for Homogenization and Quality Control in Climatological Databases, May 2008 Quality control Minimum temperature ( ) Result of automatic Quality Control by MASH Total number of errors: 2058 Maximal positive error: Minimal negative error: Example: Túrkeve(55706), February

Meeting of COST-ES0601 (HOME) and 6th Seminar for Homogenization and Quality Control in Climatological Databases, May 2008 Quality control Maximum temperature ( ) Result of automatic Quality Control By MASH Total number of errors: 3319 Maximal positive error: Minimal negative error: Example: Kecskem é t(46401), May

Meeting of COST-ES0601 (HOME) and 6th Seminar for Homogenization and Quality Control in Climatological Databases, May 2008 Quality control Precipitation ( ) Result of automatic Quality Control by MASH Total number of errors: 854 Maximal positive error: 84.0 Minimal negative error: -33.4

Base period: Meeting of COST-ES0601 (HOME) and 6th Seminar for Homogenization and Quality Control in Climatological Databases, May 2008 TN10p original homogenizedoriginal homogenized Base period: TN90p

Meeting of COST-ES0601 (HOME) and 6th Seminar for Homogenization and Quality Control in Climatological Databases, May 2008 The fitted linear trends to original and homogenized series TN10p TN90p

Meeting of COST-ES0601 (HOME) and 6th Seminar for Homogenization and Quality Control in Climatological Databases, May 2008 Extreme climate index calculations on gridded (interpolated) daily data TN10p, no sign. Gridding of homogenized daily data series was carried out by method MISH (Meteorological Interpolation based on Surface Homogenized Data Basis; Szentimrey T., Bihari Z.)

Meeting of COST-ES0601 (HOME) and 6th Seminar for Homogenization and Quality Control in Climatological Databases, May 2008 TX10p TX90p original homogenizedoriginal homogenized

Meeting of COST-ES0601 (HOME) and 6th Seminar for Homogenization and Quality Control in Climatological Databases, May 2008 The fitted linear trends to original and homogenized series TX10p TX90p

Meeting of COST-ES0601 (HOME) and 6th Seminar for Homogenization and Quality Control in Climatological Databases, May 2008 Tx10p, Tx90p, Trends on gridded data

Meeting of COST-ES0601 (HOME) and 6th Seminar for Homogenization and Quality Control in Climatological Databases, May 2008 HWDI original homogenized

Meeting of COST-ES0601 (HOME) and 6th Seminar for Homogenization and Quality Control in Climatological Databases, May 2008 R95pTOT Precipitation

Meteorológiai szélsőségek és várható alakulásuk a Kárpát-medencében, május R95pTOT

Meteorológiai szélsőségek és várható alakulásuk a Kárpát-medencében, május p=0.9 significance level p=0.9 significance level

Meteorológiai szélsőségek és várható alakulásuk a Kárpát-medencében, május p=0.8 significance level R95pTOT,

Meeting of COST-ES0601 (HOME) and 6th Seminar for Homogenization and Quality Control in Climatological Databases, May 2008 The fitted linear trends to original and homogenized series

Meeting of COST-ES0601 (HOME) and 6th Seminar for Homogenization and Quality Control in Climatological Databases, May 2008 Conclusion Extreme climate indices calculations for detection of climate change require good quality, homogeneous time series In many cases the characteristics of the estimated linear trends are unambiguously unlike on the original and homogenized time series

Thank you for your attention!