* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Climate Change Detection: The Importance of Homogenized Time
2009 United Nations Climate Change Conference wikipedia , lookup
Global warming hiatus wikipedia , lookup
Global warming controversy wikipedia , lookup
Heaven and Earth (book) wikipedia , lookup
Fred Singer wikipedia , lookup
Politics of global warming wikipedia , lookup
ExxonMobil climate change controversy wikipedia , lookup
Soon and Baliunas controversy wikipedia , lookup
Michael E. Mann wikipedia , lookup
Climate change feedback wikipedia , lookup
Climate resilience wikipedia , lookup
Global warming wikipedia , lookup
Climate change denial wikipedia , lookup
Climatic Research Unit email controversy wikipedia , lookup
Effects of global warming on human health wikipedia , lookup
Climate engineering wikipedia , lookup
Economics of global warming wikipedia , lookup
Climate governance wikipedia , lookup
North Report wikipedia , lookup
Climate change in Saskatchewan wikipedia , lookup
Carbon Pollution Reduction Scheme wikipedia , lookup
Climate change adaptation wikipedia , lookup
Effects of global warming wikipedia , lookup
General circulation model wikipedia , lookup
Climate change and agriculture wikipedia , lookup
Climate change in Tuvalu wikipedia , lookup
Solar radiation management wikipedia , lookup
Media coverage of global warming wikipedia , lookup
Climate sensitivity wikipedia , lookup
Citizens' Climate Lobby wikipedia , lookup
Climate change in the United States wikipedia , lookup
Climatic Research Unit documents wikipedia , lookup
Attribution of recent climate change wikipedia , lookup
Scientific opinion on climate change wikipedia , lookup
Public opinion on global warming wikipedia , lookup
Global Energy and Water Cycle Experiment wikipedia , lookup
Climate change and poverty wikipedia , lookup
Years of Living Dangerously wikipedia , lookup
Effects of global warming on humans wikipedia , lookup
IPCC Fourth Assessment Report wikipedia , lookup
Surveys of scientists' views on climate change wikipedia , lookup
Workshop on Climate Change Adaptation Indicators JERUSALEM, ISRAEL 24 – 26 March 2015 Climate change detection - The importance of homogenized time series Yizhak Yosef Climate Department Israel Meteorological Service Outline – Part I • Short introduction to homogenization • The integrated homogenization model used in the IMS • Results – Case study for Negba minimum temperature 1950-2012 – Quick over view on five adjusted series 1950-2012 • Conclusions and summary 2 Outline – Part II Analysis of extremes in a changing climate – Extremes Indices Part III – Avner Furshpan Climate Change in Israel – IMS findings report, March 2015 HOMOGENIZATION Most long-term climatological time series have been affected by number of non-climatic factors that make these data unrepresentative of the actual climate variation occurring over time. Homogeneity testing is preformed to ensure that time fluctuations in the data are only due to the vagaries of weather and climate. Aguilar E, Auer I, Brunet M, Peterson TC, Wieringa J. 2003. Guidelines on climate metadata and homogenization. WCDMP-No. 53, WMO-TD No. 1186. World Meteorological Organization, Geneva. Long-term climate change Apparent Real Local Directly or indirectly climatic • Station relocations • Changes in vegetation, • Change in circulation • Screen design soil or drainage • Change in planetary • Instrumentation: • Re/deforestation, albedo, ice and snow Replacement, Change in artificial lakes • Change in atmospheric calibration, maintenance • Building construction transparency (aerosols, • Change in observing times • Urbanization CO2) • Changing in averaging • Industrialization • Extraterrestrial changes methods (e.g solar constant) • Change in the close vicinity of the station Raino Heino (1996), Metadata and their role in homogenization, Proceedings of the First Seminar for Homogenization and Quality Control in Climatological Databases, Budapest, Hungary, 6-12 October 1996. Common Homogeneity Procedure Quality Control for all the data (base and reference) Collecting relevant Metadata Composing reference series Homogeneity tests (absolute & relative) Break-points detection Adjustment 6 AnClimv5 (Štěpánek, P. 2008) ACMANTv2 (Domonkos, P. 2012) HOMER_2.6 (Mestre, O. et al, 2012) RHtestsV4 (Wang, X. L & Feng, Y. 2013) R Development Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, 7 Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/. Homogenization Model at IMS Absolute AnClim, RHtestsV4 Relative methods Composing one reference series based on weighed average - AnClim HOMER ACMANT Q.C, Cluster Analysis Applying metadata k k Qi yi 2j [ x ji x j y ] / 2j j 1 j 1 Full H.T RHtestsV4 AnClim applying metadata Summarizing all the results and Establishing the Break-Points locations Adjustment Yosef, Y., I. Osetinsky, and A. Furshpan (2014), Homogenization of monthly temperature series in Israel – an integrated approach for optimal break-points detection. Proceedings of the 8th Seminar for Homogenization and Quality Control in Climatological Databases, Budapest, Hungary, 12-16 may 2014, WCDMP. WMO, Submitted. Key factors causing inhomogeneity in the Israeli climatological stations temperature data Instrumentation (calibration, upgrading to electronic sensors, types of sensors). Relocations Changing screen design and maintenance. 9 Focusing on 5 stations Long climate records. Data use after a systematic Quality Control procedure. Availability of reliable metadata. Good representation of various climate regions in Israel. The average of the mean temperature of these 5 stations represents quite well the average temperature of all Israel. Case study Negba 10 Using Cluster Analysis to choose reference stations Beit Jimal Gat 11 Dorot Qevuzat Yavne Negba Hafez Hayyim Mazkeret Besor Batya Farm Break-point detection using RHtestV4 Negba: Monthly differences between the base and reference minimum temperature series Thermometer replacement False detection Relocation & Change in design Relocation Switching sensor in 2012 The method is based on, Wang, X. L., 2008: Accounting for autocorrelation in detecting mean shifts in climate data series using the penalized maximal t or F test. Journal of Applied Meteorology and Climatology, 47, 2423–2444. Metadata were used only after the detection phase, to validate the results 12 Adjusted series and correction factors vs. Base (raw data) Negba Tmin Annual Adjustment 2.0 1.5 1.0 0.5 base 2012 2010 2008 2000 1998 1996 o C/100y TN TX Base 2.65 0.74 1.6 0.35 1.5 After 1.0 adjustment 0.5 13 base adjusted 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982 1980 1978 1976 1974 1972 1970 1968 1966 1964 1962 1960 1958 1956 1954 1952 1950 -1.5 2010 -1.0 Correction Factors 0.4 0.97 0.4 -0.1 -0.33 -0.15 2008 -0.5 Break Points 1952 1953 1960 1963 1967 1974 2006 0.0 2004 Temperature Anomaly [0C] Negba Tmax Annual Adjustment 2.0 1994 adjusted *Difference from 1961-1990 2.5 1992 1990 1988 1986 1984 1982 1980 1978 1976 1974 1972 1970 1968 1966 1964 1962 1960 1958 1956 1954 1952 1950 -1.5 Correction Factors 0.39 0.63 0.47 2006 -1.0 2004 -0.5 2002 Break Points 1965 1970 1977 0.0 2002 Temperature Anomaly [0C] 2.5 TX series results: Adjusted vs. Base 35.0 33.0 31.0 29.0 27.0 25.0 23.0 21.0 19.0 14 Eilat adj Eilat base Negba adj Negba base Beit Jimal adj Beit Jimal base Jerusalem adj Jerusalem base Zefat adj Zefat base 2012 2010 2008 2006 2004 2002 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982 1980 1978 1976 1974 1972 1970 1968 1966 1964 1962 1960 1958 1956 1954 1952 1950 17.0 TX series results: Adjusted vs. Base 15 TX series results: Adjusted vs. Base 16 TN series results: Adjusted vs. Base 22.0 20.0 18.0 16.0 14.0 12.0 17 Eilat adj Eilat base Negba adj Negba base Beit Jimal adj Beit Jimal base Jerusalem adj Jerusalem base Zefat adj Zefat base 2012 2010 2008 2006 2004 2002 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982 1980 1978 1976 1974 1972 1970 1968 1966 1964 1962 1960 1958 1956 1954 1952 1950 10.0 TN series results: Adjusted vs. Base 18 TN series results: Adjusted vs. Base 19 Causes of the inhomogeneities identified unknown 23% instrumentation 48% enviroment 6% screen (design & maintenance) 13% relocation 10% Frequency distribution of break magnitudes (No. of breaks, monthly resolution) Conclusions and summary • Long time series have been affected by a number of non climatic factors (relocation, instrumentation, screen design etc.). • Some changes may cause sharp discontinuities while other gradual change biases in the data. • In average there are 4-5 break-points in each of the examined stations. • The annual average correction factors interval for the 5 stations are: Tx: [-1.23, 0.71] [oC] Tn: [-0.48, 1.09] [oC]. • Before analyzing trends for climate change it is important first to do quality control and then to homogenized (find the break-points and correct the bias) the series. An inhomogenized time series can lead to wrong interpretation of the climate Part II – Extreme Indices World Meteorological Organization (2009), Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation, Tech. Rep. 72, Geneva, Switzerland. Analysis of extremes in a changing climate Commission for Climatology (CCI) Climate Variability and Predictability (CLIVAR) Joint WMO-IOC Technical Commission for Oceanography and Marine Meteorology (JCOMM) Expert Team on Climate Change Detection and Indices (ETCCDI) Extreme Indices In order to gain a uniform perspective on observed changes in weather and climate extremes a core set of 27 indices for moderate/soft temperature and precipitation extremes was defined. 1. ID FD0 Indicator name Frost days Definitions Annual count when TN(daily minimum)<0ºC UNITS Days 2. SU25 Summer days Annual count when TX(daily maximum)>25ºC Days 3. ID0 Ice days Annual count when TX(daily maximum)<0ºC Days 4. TR20 Tropical nights Days 5. GSL 6. TXx Max Tmax Annual count when TN(daily minimum)>20ºC Annual (1st Jan to 31st Dec in NH, 1st July to 30th June in SH) count between first span of at least 6 days with TG>5ºC and first span after July 1 (January 1 in SH) of 6 days with TG<5ºC Monthly maximum value of daily maximum temp 7. TNx Max Tmin Monthly maximum value of daily minimum temp ºC 8. TXn Min Tmax Monthly minimum value of daily maximum temp ºC TNn Min Tmin Monthly minimum value of daily minimum temp ºC 10. TN10p Cool nights Percentage of days when TN<10th percentile % 11. TX10p Cool days Percentage of days when TX<10th percentile % 12. TN90p Warm nights Percentage of days when TN>90th percentile % 13. TX90p Warm days 14. WSDI Percentage of days when TX>90th percentile Annual count of days with at least 6 consecutive days when TX>90th percentile Annual count of days with at least 6 consecutive days when TN<10th percentile % Warm spell duration indicator Cold spell duration indicator Diurnal temperature range Max 1-day precipitation amount Max 5-day precipitation amount Simple daily intensity index Number of heavy precipitation days Number of very heavy precipitation days Number of days above nn mm Consecutive dry days Monthly mean difference between TX and TN ºC Monthly maximum 1-day precipitation mm Monthly maximum consecutive 5-day precipitation mm Annual total precipitation divided by the number of wet days (defined as PRCP>=1.0mm) in the year mm/day Annual count of days when PRCP>=10mm Days Annual count of days when PRCP>=20mm Days 9. 15. CSDI 16. DTR 17. RX1day 18. Rx5day 19. SDII 20. R10 21. R20 22. Rnn 23. CDD 24. CWD 25. R95p 26. R99p 27. PRCPTOT Growing season Length Annual count of days when PRCP>=nn mm, nn is user defined threshold Maximum number of consecutive days with RR<1mm Consecutive wet days Maximum number of consecutive days with RR>=1mm Very wet days Annual total PRCP when RR>95th percentile Extremely wet days Annual total PRCP when RR>99th percentile Annual total wet-day Annual total PRCP in wet days (RR>=1mm) precipitation Days ºC An internationally coordinated core set of 27 indices (moderate/soft extremes) Days Days Days Days Days mm mm mm For detailed calculation see: World Meteorological Organization (2009), Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation, Tech. Rep. 72, Geneva, Switzerland. http://www.clivar.org/panels-and-workinggroups/etccdi/etccdi.php 27 core indices 16 indices for temperature 11 indices for precipitation • The indices describe different aspects of moderate temperature and precipitation extremes, including frequency, intensity and duration. • The indices are based on daily temperature and/or daily precipitation amount. • Some are based on fixed thresholds. • Other indices are based on thresholds that vary from location to location and thresholds are typically defined as a percentile of the relevant data series. Core Indices SECTOR Indicators SECTOR SECTOR … Thank You for your attention! Email: [email protected] 29 Different screen design 1952 1977 2004 Jerusalem – Center (Generally building) From Conventional Weather Station to AWS Zefat Har Kenaan – Change in the close vicinity of the screen 1939 1957 1952 2005