Summary
A methodology to estimate the space-time distribution of daily mean temperature under climate change is developed and applied to a central Nebraska case study. The approach is based on the analysis of the Markov properties of atmospheric circulation pattern (CP) types, and a stochastic linkage between daily (here 500hPa) CP types and daily mean temperatures. Historical data and general circulation model (GCM) output of daily CP corresponding to 1 × CO2 and 2 × CO2 scenarios are considered. The relationship between spatially averaged geopotential height of the 500 hPa surface — within each CP type — and daily mean temperature is described by a nonparametric regression technique. Time series of daily mean temperatures corresponding to each of these cases are simulated and their statistical properties are compared. Under the climate of central Nebraska, the space-time response of daily mean temperature to global climate change is variable. In general, a warmer climate appears to cause about 5°C increase in the winter months, a smaller increase in other months with no change in July and August. The sensitivity of the results to the GCM utilized should be considered.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bardossy, A., Caspray, H., 1990; Detection of climate change in Europe by analyzing European circulation patterns from 1881 to 1989.Theor. Appl. Climatol. 42, 155–167.
Bardossy, A., Plate, E., 1990: Modeling daily rainfall using a semi-Markov representation of circulation pattern occurrence.J. Hydrol. 66, 33–47.
Bardossy, A., Plate, E., 1992: Space-time model for daily rainfall using atmospheric circulation patterns.Water Resour. Res. 38, 1247–1260.
Barry, R. G., Kiladis, G., Bradley, R. S., 1981: Synoptic climatology of the Western United States in relation to climatic fluctuations during the twentieth century.J. Climatol. 1, 97–113.
Beniston, M., Sanchez, J. P., 1992: An example of climaterelevant processes unresolved by present-day general circulation models.Speedup 6, 19–25.
Boer, G. J., McFarlane, N. A., Laprise, R., Henderson, J. D., Blanchet, J. P., 1984: The Canadian climate centre spectral atmospheric general circulation model.Atmosphere-Ocean 22, 397–429.
Bogardi, I., Matyasovszky, I., Bardossy, A., Duckstein, L., 1992: Estimation space-time local hydrological quantities under climate change.Proceedings 5th Interntional Meeting on Statistical Climatology (Toronto, Canada) pp. 95–99.
Dickinson, R. E., Errico, R. M., Giorgi, F., Bates, G. T., 1989: A regional climate model for the western United States.Climatic Change 15, 382–422.
Essenwanger, O. M., 1976:Applied Statistics in Atmospheric Research. Amsterdam, New York: Elsevier, 400 pp.
Giorgi, F., Mearns, L. O., 1991: Approaches to the simulation of regional climate change: a review.Rev. Geophys. 29, 191–216.
Hansen, J., Lacis, A., Rind, D., Russel, G., Stone, P., Fung, I., Ruedy, R., Lerner, J., 1984: Climate sensitivity: analysis of feedback mechanisms, In: Climate processes and climate sensitivity.Geophys. Monograph 29, 130–163.
Houghton, J. T., Jenkins, G. J., Ephraums J. J., (eds.), 1990:Climate Change. The IPCC Scientific Assessment. Intergovernmental Panel on Climate Change. New York: Cambridge University Press, 364 pp.
Lamb, H. H., 1977:Climate, Present, Past and Future. Vol 2: Climatic History and the Future. London: Methuen, 835 p.
MacQueen, J., 1967: Some methods for classification and analysis of multivariate observations.Proceedings, 5th Berkeley Symp. on Math. Stat. and Prob. 1, 281–297.
Manabe, S., Wetherald, R. T., 1980: On the distribution of climate change resulting from an increase in CO2 content of the atmosphere.J. Atmos. Sci. 37, 99–118.
Matyasovszky, I., 1992: Nonparametric regression methods for trend estimation of climatological time series.Proceedings 12th Conference on Probability and Statistics in Atmospheric Sciences (Toronto, Canada) pp. 32–36.
Matyasovszky, I., Bogardi, I., Bardossy, I., Duckstein, L., 1993: Estimation of local precipitation statistics reflecting climate change.Water Resour. Res. 29, 3955–3968.
Müller, H. G., 1988:Nonparametric Regression Analysis of Longitudinal Data. Berlin: Springer Verlag, 168 p. (Lecture Notes in Statistics 46).
Müller, H. G., 1991: Smooth optimum kernel estimators near endpoints.Biometrika 78, 521–530.
Nadaraya, E. A., 1964: On estimating regression.Theory of Probability and its Application 9, 141–142.
Simmons, A. J., Bengston, L., 1988: Atmospheric general circulation models: Their design and use for climate studies. In M. Schlesinger,Physically-Based Modelling and Simulation of Climate and Climatic Change, Vol. II,NATO ASI Ser. Boston: Kluwer Academic 627–652.
Sowden, I. P., Parker, D. E., 1981: A study of climatic variability of daily Central England temperatures in relation to the Lamb synoptic types.J. Climatol. 1, 3–10.
Toth, Z., Szentimrey, T., 1990: The binormal distribution: distribution for representing asymmetrical but normal-like weather elements.J. Climate 3, 128–136.
Watson, G. S., 1964: Smooth regression analysis.Sankhya, Ser. A. 26, 359–372.
Author information
Authors and Affiliations
Additional information
On leave from the Department of Meteorology, Eötvós Loránd University, Budapest, Hungary.
With 14 Figures
Rights and permissions
About this article
Cite this article
Matyasovszky, I., Bogardi, I., Bardossy, A. et al. Local temperature estimation under climate change. Theor Appl Climatol 50, 1–13 (1994). https://doi.org/10.1007/BF00864897
Received:
Revised:
Issue Date:
DOI: https://doi.org/10.1007/BF00864897