Testing the stability of transfer functions |
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| Affiliation: | 1. Technische Universität München, Ökoklimatologie, Freising, Germany;2. Technische Universität München, Land Surface-Atmosphere Interactions, Freising, Germany;3. Technische Universität München, Institute for Advanced Study, Garching, Germany;1. Department of Agricultural, Food and Environmental Sciences, Marche Polytechnic University, Via Brecce Bianche 10, 60131 Ancona, Italy;2. Department of Geography, University of Cambridge, Downing Place, CB2 3EN Cambridge, United Kingdom;1. Ecoclimatology, Department of Ecology and Ecosystem Management, Technische Universität München, Hans-Carl-von-Carlowitz-Platz 2, 85354 Freising, Germany;2. Land Surface-Atmosphere Interactions, Department of Ecology and Ecosystem Management, Technische Universität München, Hans-Carl-von-Carlowitz-Platz 2, 85354 Freising, Germany;3. Institute for Advanced Study, Technische Universität München, Lichtenbergstr 2a, 85748 Garching, Germany;1. Slovenian Forestry Institute, Department of Forest Yield and Silviculture, Večna pot 2, 1000, Ljubljana, Slovenia;2. Jožef Stefan Institute, Department of Knowledge Technologies, Jamova cesta 39, 1000, Ljubljana, Slovenia;3. Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000, Ljubljana, Slovenia;1. Institute of Forest Sciences, Chair of Forest Growth and Dendroecology, Albert-Ludwigs-University Freiburg, Tennenbacher Str. 4, 79106 Freiburg, Germany;2. Department of Forest and Wood Science, Stellenbosch University, South Africa |
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| Abstract: | In dendroclimatology, testing the stability of transfer functions using cross-calibration verification (CCV) statistics is a common procedure. However, the frequently used statistics reduction of error (RE) and coefficient of efficiency (CE) merely assess the skill of reconstruction for the validation period, which does not necessarily reflect possibly instable regression parameters. Furthermore, the frequently used rigorous threshold of zero which sharply distinguishes between valid and invalid transfer functions is prone to an underestimation of instability. To overcome these drawbacks, we here introduce a new approach – the Bootstrapped Transfer Function Stability test (BTFS). BTFS relies on bootstrapped estimates of the change of model parameters (intercept, slope, and r2) between calibration and verification period as well as the bootstrapped significance of corresponding models. A comparison of BTFS, CCV and a bootstrapped CCV approach (BCCV) applied to 42,000 pseudo-proxy datasets with known properties revealed that BTFS responded more sensitively to instability compared to CCV and BCCV. BTFS performance was significantly affected by sample size (length of calibration period) and noise (explained variance between predictor and predictand). Nevertheless, BTFS performed superior with respect to the detection of instable transfer functions in comparison to CCV. |
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| Keywords: | Dendroclimatology Climate reconstruction Verification Coefficient of efficiency Reduction of error Bootstrapped Transfer Function Stability test |
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