A novel method for the homogenization of daily temperature series and its relevance for climate change analysis

Journal of Climate

Volumn 23, Issue 19, 2010, Pages 5325-5331

  Toreti, Andrea ^a,b   Kuglitsch, Franz G ^a   Xoplaki, Elena ^a,c   Luterbacher, Jürg ^d   Wanner, Heinz ^a  

^a UNIVERSITY OF BERN   (Switzerland)

^b INSTITUTE FOR ENVIRONMENTAL PROTECTION AND RESEARCH ISPRA   (Italy)

^c CYPRUS INSTITUTE   (Cyprus)

^d JUSTUS LIEBIG UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

AUTO-CORRELATED DATA; CHANGE ANALYSIS; CLIMATE CHANGE ASSESSMENT; DAILY TEMPERATURES; EASTERN MEDITERRANEAN; HIGHER ORDER; HOMOGENIZATION PROCEDURE; INHOMOGENEITIES; NOVEL METHODS; REGRESSION PARAMETERS; SMALL SAMPLES; TREND ESTIMATION;

FIBER OPTICS; HOMOGENIZATION METHOD; TIME SERIES;

CLIMATE CHANGE;

CLIMATE CHANGE; DIURNAL VARIATION; ESTIMATION METHOD; SIMULATION; TEMPERATURE EFFECT; TIME SERIES;

MEDITERRANEAN SEA; MEDITERRANEAN SEA (EAST);

EID: 77958110701     PISSN: 08948755     EISSN: None     Source Type: Journal    
DOI: 10.1175/2010JCLI3499.1     Document Type: Article

References (32)

1. Aguilar, E., I. Auer, M. Brunet, T. C. Peterson, and J. Wieringa, 2003: Guidance on metadata and homogenization. WMO TD 1186, 53 pp.
2. Aguilar, E., M.Brunet, J. Sigró,F.S.Rodrigo,Y. L.Rico,andD.R.Alvarez, 2008: Homogenization of Spanish temperature series on a daily resolution:Astep forward towards an analysis of extremes in the Iberian Peninsula. Proc. Seventh European Conf. on Applied Climatology, Amsterdam, the Netherlands, European Meteor. Soc., A-00-697. [Available online at http://www.emetsoc.org/annual_meetings/documents/Aguilar_ECAC_2008.pdf.]
3. Alexandersson, H., and A. Moberg, 1997: Homogenization of Swedish temperature data. Part I: Homogeneity test for linear trends. Int. J. Climatol., 17, 25-34.
4. Bosq, D., 1998: Nonparametric Statistics for Stochastic Processes: Estimation and Prediction. Springer-Verlag, 232 pp.
5. Caussinus, H., and O. Mestre, 2004: Detection and correction of artificial shifts in climate series. Appl. Stat., 53, 405-425.
6. Cleveland, W. S., and S. J.Devlin, 1988: Locallyweighted regression: An approach to regression analysis by local fitting. J.Amer. Stat. Assoc., 83, 596-610.
7. Coles, S., 2001: An Introduction to Statistical Modeling of Extreme Values. Springer-Verlag, 224 pp.
8. Currie, I., and M. Durban, 2002: Flexible smoothing with P-splines: A unified approach. Stat. Model., 2, 333-349.
9. Davison, A., and R. Smith, 1990: Model for exceedances over high thresholds. J. Roy. Stat. Soc., 52B, 393-442.
10. Della-Marta, P. M., and H. Wanner, 2006: A method of homogenizing the extremes and mean of daily temperature measurements. J. Climate, 19, 4179-4197.
11. Della-Marta, P. M., M. R. Haylock, J. Luterbacher, and H. Wanner, 2007: Doubled length of western European summer heat waves since 1880. J.Geophys.Res., 112,D15103, doi:10.1029/2007JD008510.
12. Durban,M., and I. Currie, 2003: A note on P-spline additive models with correlated errors. Comput. Stat., 18, 251-262.
13. Eilers, P. H. C., and B. D. Marx, 1996: Flexible smoothing with B-splines and penalties. Stat. Sci., 11, 89-121.
14. Estévez, G., and P. Vieu, 2003: Nonparametric estimation under long memory dependence. J. Nonparametric Stat., 15, 535-551.
15. Hart, J. D., and P. Vieu, 1990: Data-driven bandwidth choice for density estimation based on dependent data. Ann. Stat., 18, 873-890.
16. Harville, D., 1977: Maximum likelihood approaches to variance component estimation and to related problems. J. Amer. Stat. Assoc., 72, 320-338.
17. Hosking, J. R. M., 1990: L-Moments: Analysis and estimation of distributions using linear combinations of order statistics. J. Roy. Stat. Soc., 52B, 105-124.
18. Hurvich, C. M., and C. L. Tsai, 1989: Regression and time series model selection in small samples. Biometrika, 76, 297-307.
19. Krivobokova, T., and G. Kauermann, 2007: A note on penalized spline smoothing with correlated errors. J. Amer. Stat. Assoc., 580, 1328-1337.
20. Krivobokova, T., G. Crainiceanu, and G. Kauermann, 2008: Fast adaptive penalized splines. J. Comput. Graph. Statist., 17, 1-20.
21. Kuglitsch, F. G., A. Toreti, E. Xoplaki, P. M. Della-Marta, J. Luterbacher, and H. Wanner, 2009: Homogenization of daily maximum temperature series in the Mediterranean. J. Geophys. Res., 114, D15108, doi:10.1029/2008JD011606.
22. Moberg, A., and Coauthors, 2006: Indices for daily temperature and precipitation extremes in Europe analyzed for the period 1901-2000. J.Geophys. Res., 111,D22106, doi:10.1029/2006JD007103.
23. Opsomer, J., Y. Wang, and Y. Yang, 2001: Nonparametric regression with correlated errors. Stat. Sci., 16, 134-153.
24. Patterson, H. D., and R. Thompson, 1971: Recovery of inter-block information when block sizes are unequal. Biometrika, 58, 545-554.
25. Ruppert, D., 2002: Selecting the number of knots for penalized splines. J. Comput. Graph. Statist., 11, 735-757.
26. Shao, J., 2003: Mathematical Statistics. Springer-Verlag, 591 pp.
27. Sköld, M., 2001: A bias correction for cross-validation bandwidth selection when a kernel estimate is based on dependent data. J. Time Ser. Anal., 22, 493-503.
28. Vincent, L. A., X. Zhang, B. R. Bonsal, and W. D. Hogg, 2002: Homogenization of daily temperatures over Canada. J. Climate, 15, 1322-1334.
29. Wang, L., 2007: Kernel type smoothed quantile estimation under long memory. Stat. Pap., 51, 57-67, doi:10.1007/s00362-007-0115-y.
30. Wang, X. L., 2008: Accounting for autocorrelation in detecting mean shifts in climate data series using the penalized maximal t or f test. J. Appl. Meteor. Climatol., 47, 2423-2444.
31. Wang, X. L., Q. H. Wen, and Y. Wu, 2007: Penalized maximal t test for detecting undocumented mean change in climate data series. J. Appl. Meteor. Climatol., 46, 916-931.
32. Wilks, D. S., 1999: Simultaneous stochastic simulation of daily precipitation, temperature and solar radiation at multiple sites in complex terrain. Agric. For. Meteor., 96, 85-101.