2003
DOI: 10.1029/2001jb001579
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Weichselian temperatures from geothermal heat flow data

Abstract: [1] We report an analysis of geothermal heat flow density (HFD) in the Fennoscandian Shield and East European Platform showing a systematic variation with depth. The HFD data (1352 values) averaged in 25 m depth intervals and in 1°Â 2°latitude-longitude areas increase from $35-40 mW m À2 in the first 500 m to $45 mW m À2 at 1000 m, finally reaching $50 mW m À2 between 1000 and 3000 m and deeper. We attribute this variation to long-term climatic changes in ground surface temperatures (GST) during the Weichselia… Show more

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Cited by 55 publications
(50 citation statements)
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“…In prior work, Huang et al (1997) and Kukkonen and Joeleht (2003) obtained long averaged temperature histories for the globe and East-European Platform (including Fennoscandia), respectively. Both studies used the Global Heat Flow Data Base of the International Heat Flow Commission of IASPEI (Pollack et al, 1993).…”
Section: Introductionmentioning
confidence: 99%
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“…In prior work, Huang et al (1997) and Kukkonen and Joeleht (2003) obtained long averaged temperature histories for the globe and East-European Platform (including Fennoscandia), respectively. Both studies used the Global Heat Flow Data Base of the International Heat Flow Commission of IASPEI (Pollack et al, 1993).…”
Section: Introductionmentioning
confidence: 99%
“…Both studies used the Global Heat Flow Data Base of the International Heat Flow Commission of IASPEI (Pollack et al, 1993). The estimated Pleistocene/Holocene global warming was less than 1.5 K (Huang et al, 1997) and 8±4.5 K (Kukkonen and Joeleht, 2003). Both studies, however, may have significantly underestimated the degree of warming.…”
Section: Introductionmentioning
confidence: 99%
“…Because of the very small number of parameters, these methods of inversion are not computationally intensive. The Monte Carlo method, which has been used by Mareschal et al (1999) and Kukkonen and Jõeleht (2003), explores the entire parameter space and requires larger computational resources than the other methods. In this study, we have used SVD to find the GST history because of its simplicity and then used a Monte Carlo procedure to determine the range of model parameters that satisfy the data within some error bounds.…”
Section: Inversionmentioning
confidence: 99%
“…(9) to assess the fit of the SVD inversion with respect the initial anomaly profile. A Monte Carlo procedure was applied (Mareschal et al, 1999;Kukkonen and Jõeleht, 2003;Chouinard et al, 2007) by randomly perturbing the model parameters to find the range of GST histories that fit the data within a maximum root mean square (RMS) error less or equal than the difference between the forward-modeled SVD reconstruction and the anomaly. Using the Monte Carlo approach to invert the temperature profiles is particularly inefficient because it requires a very large number of simulations to explore the entire parameter space.…”
Section: Forward Modelmentioning
confidence: 99%
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