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Earth Surface Dynamics An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/esurf-2019-3
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-2019-3
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 04 Feb 2019

Research article | 04 Feb 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Earth Surface Dynamics (ESurf).

Detection and Explanation of Spatiotemporal Patterns in Late Cenozoic Palaeoclimate Change Relevant to Earth Surface Processes

Sebastian G. Mutz and Todd A. Ehlers Sebastian G. Mutz and Todd A. Ehlers
  • Department of Geosciences, University Tübingen, D-72074 Tübingen, Germany

Abstract. Detecting and explaining differences between palaeoclimates can provide valuable insight into climatic tipping points and a useful framework of information for Earth scientists investigating processes that are affected by climate change over geological time. We apply a combination of multivariate cluster- and discriminant analysis techniques to a set of consistently set-up high-resolution palaeoclimate simulations conducted with the ECHAM5 climate model. A pre-industrial (PI) climate simulation serves as the control experiment, which is compared to a suite of simulations of Late Cenozoic climates, namely a Mid-Holocene (MH, ca. 6.5 ka), Last Glacial Maximum (LGM, ca. 21 ka) and Pliocene (PLIO, ca. 3 Ma) climate. For each of the study regions (Western South America, Europe and Himalaya–Tibet and South Alaska), differences in climate are subjected to geographical clustering to identify dominant modes of climate change and their spatial extent for each time slice comparison (PI-MH, PI-LGM and PI-PLIO). The selection of climate variables for the cluster analysis is made on the basis of their relevance to Earth surface processes and includes 2 m air temperature, 2 m air temperature amplitude, consecutive freezing days, freeze-thaw days, maximum precipitation, consecutive wet days, consecutive dry days, zonal wind speed and meridional wind speed. We then apply a two-class multivariate discriminant analysis to simulation pairs PI-MH, PI-LGM and PI-PLIO to evaluate and explain the discriminability between climates within each of the anomaly clusters. Changes in ice cover create the most distinct and stable patterns of climate change, and create the best discriminability between climates in western Patagonia. The distinct nature of European palaeoclimates is mostly explained by changes in 2 m air temperature (MH, LGM, PLIO), consecutive freezing days (LGM) and consecutive wet days (PLIO). These factors typically contribute 30 %–50 %, 10 %–40 % and 10 %–30 % respectively to climate discriminability. Finally, our results identify regions particularly prone to changes in precipitation-induced erosion and temperature-dependent physical weathering.

Sebastian G. Mutz and Todd A. Ehlers
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Status: final response (author comments only)
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Sebastian G. Mutz and Todd A. Ehlers
Sebastian G. Mutz and Todd A. Ehlers
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Latest update: 24 Apr 2019
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Short summary
We apply machine learning techniques to quantify and explain differences between recent palaeoclimates with regards to factors that are important in shaping the Earth's surface. We find that changes in ice cover, near surface air temperature and rainfall duration create the most distinct differences. We also identify regions particularly prone to changes in rainfall and temperature controlled erosion, which will help with the interpretation of erosion rates and geological archives.
We apply machine learning techniques to quantify and explain differences between recent...
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