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Data-Based Models for Global Temperature Variations

Published

Author(s)

Bert W. Rust, Barend J. Thijsse

Abstract

This paper presents two data-based models for the measured time series of global annual average tropospheric temperatures. One model is a smoothing spline fit designed to give an optimal separation of signal from noise. The other combines an optimal spline fit to the measured record of carbon dioxide concentration in the atmosphere with a well known 70 year cycle in the temperatures. It assumes a simple linear relation between changes in temperature and changes in the carbon dioxide concentration. When the cycle is added to the model, its fit to the temperature data is very similar to the optimal spline fit. The differences between the two fits are smaller in magnitude than the residuals for either one of them.
Proceedings Title
CSC'07: Proceedings of the 2007 International Conference on Scientific Computing
Conference Dates
June 25-28, 2007
Conference Location
Las Vegas, NV, USA
Conference Title
CSC'07: 2007 International Conference on Scientific Computing

Citation

Rust, B. and Thijsse, B. (2007), Data-Based Models for Global Temperature Variations, CSC'07: Proceedings of the 2007 International Conference on Scientific Computing, Las Vegas, NV, USA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=51127 (Accessed April 20, 2024)
Created December 31, 2006, Updated October 12, 2021