Samec - Regression analysis of relations among main Quaternary environmental changes indicators
- 1. Regression analysis of relations among Quaternary environmental change indicators Pavel Samec
- 2. Content • Introduction - Glacial/interglacial cycles - The polyglacism theory • Material - Loess/paleosol proxy series - Deep-sea mud proxy series - Ice core proxy series • Methods - Exploratory data analysis - Interpolation - Multiple regression - Logistic regression • Results and Discussion • Summary • References
- 3. Introduction The Quaternary is a period when the geological presence has developed. The Quaternary Period is characterized by regular alternation of major environmental changes with the intensity of glacial/interglacial. • The glacial is an event of glaciations characterized by expansion of continental and mountain glaciers, global marine regression, global reduction of vegetation biomass and expansion of terrestrial sedimentary environments. • The interglacial is an event of interglaciations characterized by minimal glaciations, global marine transgression, global growth of vegetation biomass and an intensive soil formation ont the most of land. The polyglacism theory deals with common variability of marine and terrestrial sedimentation features in relation to the variability of the external physical environment that indicate the global impacts of multiple oscillations glacial/interglacial. The aim of the study was using of the logistic regression for better description of assumed polyglacial relationships.
- 4. Material • Data - Loess/paleosol series (Chinese Loess Plateau; 6.9-0 Ma; Sun et al. 2012) - Deep sea d18O (East Pacific; 470-4 Ka; Lea et al. 2000) - Ice core (East Antarctica; 803-2 Ka; Barnola et al. 1999; Petit et al. 2000) • Analysed periods - Middle-Upper Pleistocene (470-12 Ka) - Upper Pleistocene – Holocene (126-1 Ka)
- 5. The Central Chinese Loess Plateau eolian sediment magnetic susceptibility (MS) (data according to Sun et al. 2012). 350 300 250 200 150 100 50 0 6.8 6.4 6.0 5.7 5.4 5.1 4.9 4.6 4.1 3.8 3.6 3.2 3.0 2.7 2.4 2.1 1.7 1.5 1.2 0.9 0.6 0.3 0.0 MS (10-8 m3/kg) Dating (Ma) The East Equatorial Pacific average sea surface temperatures and oxygen proxy record (data according to Lea et a. 2000). d18O T (°C) 3 2 1 0 -1 -2 -3 32 28 24 20 16 461 384 306 245 176 124 80 36 4 Dating (Ka) sea surface temperature oxygen isotopical signal
- 6. Methods • Global temperature deviations were main features of the glacial/interglacial cycle. • Glacial – 0 • Interglacial - 1 DT (°C) CO2 (ppm) 4 2 0 -2 -4 -6 -8 -10 300 280 260 240 220 200 180 314 231 136 2 carbon dioxide (ppm) Dating (Ka) temperature deviations (°C) CO2 (ppm) 300 275 250 225 200 175 150 -10 -8 -6 -4 -2 0 2 DT (°C) North Atlantic East Equatorial Pacific Antarctica dCO2 1.00 0.75 0.50 0.25 0.00 0.00 0.30 0.60 0.90 1.20 1.50 Ttropy/Tpolar
- 7. • Exploratory data analysis - Test on normality distribution - Regression diagnosis - Linear correlation and regression • Interpolation - Transformation according to 0-1 limits • Regression analysis - Linear regression - Multiple regression - Logistic regression Data calibration EDA Linear regression Binomical interpolation Multiple regression Logistic regression
- 8. Results and discussion Exploratory linear regression of basic Quaternary sedimentation core properties. y – receptor; x – predictor; F –Fischer-Snedecorov’s testing criterion; t – Student’s t-test criterion; r ‒ correlation coefficient; SC – Scott’s test on multicolinearity; C-W – Cook-Weisberg’s test on heteroscedasticity; J-B – Jarque-Berrae’s test on normality of residues; Wa – Wald’s test on autocorrelation.
- 9. Linear regression of the compared facial proxy data
- 10. Multiple regression of compared environmetal proxy indicators Multiple regression of basic Quaternary sea (d18O) and terrestrial environmental indicators (CaCO3). SD – standard deviation; B-CT – Box-Cox transformation; MS – magnetic susceptibility; GSM – grain size median.
- 11. Logistic regression of compared environmental proxy indicators
- 12. Summary • Changes in the basic soil properties of a loess/paleosol sequences reliably do not indicate changes in the intensity of glacial/interglacial cycles between the Middle and Upper Pleistocene. • Changes in the basic soil properties of a loess/paleosol sequences have been reflecting climatic changes statistically more significantly than the deep-sea sedimentation since the Upper Pleistocene (cycle eem‒visla) . • Correlations of atmospheric CO2 and surface temperatures are greater than correlation of other polyglacial phenomenas. • Linear regression revealed on the assumption that the dependences of soil properties were smaller than polyglacial relations of other environmental indicators. • Logistic regression suggested that temporal variability in feedbacks between climatic change predictors and properties of forming sediments may be cause of the lack of a simple Quaternary climatic change indication.
- 13. References • BARNOLA J.M. et al. (1999): Historical CO2 record from the Vostok ice core. In: Trends: A Compendium of Data on Global Change. U. S. Department of Energy Oak Ridge. • HEIKKINEN R.K. et al. (2006): Methods and uncertainties in bioclimatic envelope modelling under climate change. Progress in Physical Geography 30: 6751‒6777. • KUKLA J. (1978): The Classical European Glacial Stages: Correlation with deep-sea sediments. Transactions of the Nebraska Academy of Science 6: 57–93. • KUKLA G., CÍLEK V. (1996): Plio-Pleistocene megacycles: record of climate and tectonics. Palaeogeography, Palaeoclimatology, Palaeoecology 120: 171‒194. • LEA D.W. et al. (2000): Climate impact of late Quaternary equatorial Pacific sea surface temperature variations. Science 289: 1719–1724. • OSBORN J. W. (2010): Improving your data transformation: Applying the Box-Cox transformation. Practical Assessment, Research & Evaluation 15: 2‒9. • PETIT J.R. (1999): Climate and atmospheric history of the past 420,000 years from the Vostok ice core, Antarctica. Nature 399: 429–436. • SUN Y. (2012): Seven million years of wind and precipitation variability on the Chinese Loess Plateau. Earth and Planetary Science Letters 297: 525–535.