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Reconstructing past vegetation with Random Forest Machine Learning, sacrificing the dynamic response for robust results
Stockholm University, Faculty of Science, Department of Physical Geography.ORCID iD: 0000-0001-9908-6091
Stockholm University, Faculty of Science, Department of Physical Geography.
Stockholm University, Faculty of Science, Department of Physical Geography.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Vegetation is an important feature in the Earth system, providing a direct link between the biosphere and atmosphere. As such, a representative vegetation pattern is needed to accurately simulate climate. We attempt to reconstruct past and present vegetation with a data driven approach, to test if this allows us to create robust global and regional vegetation patterns. The motivation for this stems from the possibility of avoiding circular arguments when studying past time periods where vegetation is used to reconstruct climate, and climate is used to construct vegetation. By using the Random Forest machine learning tool, we train the vegetation reconstruction with available biomized pollen data of present and past conditions and are able to produce reasonable broad-scale vegetation patterns for the Pre-Industrial and the Mid-Holocene together with a few modeled climate variables. We test the methods robustness by introducing a systematic temperature bias based on existing climate model spread and compare the result with that of LPJ-GUESS, a process-based dynamic global vegetation model. Results prove that the Random Forest approach is able to produce robust patterns for periods and regions well constrained by evidence, but fails when evidence is scarce. The robustness is achieved by sacrificing a dynamic vegetation response to a changing climate.

National Category
Climate Science Physical Geography Other Earth Sciences
Research subject
Physical Geography
Identifiers
URN: urn:nbn:se:su:diva-183518OAI: oai:DiVA.org:su-183518DiVA, id: diva2:1454702
Available from: 2020-07-20 Created: 2020-07-20 Last updated: 2025-02-01Bibliographically approved
In thesis
1. Northern Permafrost Region Soil Carbon Dynamics since the Last Glacial Maximum: a terrestrial component in the glacial to interglacial carbon cycle
Open this publication in new window or tab >>Northern Permafrost Region Soil Carbon Dynamics since the Last Glacial Maximum: a terrestrial component in the glacial to interglacial carbon cycle
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

At the Last Glacial Maximum (LGM), after ~100,000 years of relatively cold temperatures and progressively lower atmospheric carbon dioxide (CO2) concentrations, CO2 levels reached ~180 ppm, which is less than half of what we see today in a much warmer world (~400 ppm). Although much of this increase since the LGM is due to human-induced emissions, about 100 ppm of this increase can be attributed to natural variations seen over glacial to interglacial cycles. The sources for this natural CO2 rise remain unclear despite considerable efforts to constrain its origin. This thesis attempts to describe and quantify the role of soil carbon in this context, with emphasis on the permafrost hypothesis, which states that a shift from glacial to interglacial conditions released permafrost soil carbon to the atmosphere during the deglaciation. We present empirical estimates of the change in the Northern permafrost area between the LGM and present, and the associated soil carbon stock changes. We also partition these soil carbon stock changes at millennial intervals to capture not only the size but the timing of change. We find that the soil carbon stocks north of the Tropics decreased after the LGM to reach a minimum around 10,000 years ago, after which stocks increased to more than compensate for past losses. This may present part of a solution to untangle the marine and atmospheric 13C record, where the marine records suggest that the terrestrial carbon stock has grown since the LGM, while the atmospheric record also indicates terrestrial losses. To estimate the mineral soil carbon stocks, we have relied on vegetation reconstructions. Some of these reconstructions were created with a novel data-driven machine learning approach. This method may facilitate robust vegetation reconstruction when evidence of past conditions is readily available. Results in this thesis highlight the importance of permafrost, loess deposits and peatlands when considering the soil carbon cycle over long time scales.

Place, publisher, year, edition, pages
Stockholm: Department of Physical Geography, Stockholm University, 2020. p. 50
Series
Dissertations in Physical Geography, ISSN 2003-2358 ; 6
Keywords
Soil organic carbon, Permafrost, Peat, Loess, Vegetation, Biome reconstruction, Last Glacial Maximum, Deglaciation, Glacial-interglacial cycle, Carbon cycle
National Category
Physical Geography
Research subject
Physical Geography
Identifiers
urn:nbn:se:su:diva-183520 (URN)978-91-7911-234-9 (ISBN)978-91-7911-235-6 (ISBN)
Public defence
2020-09-18, De Geersalen, Geovetenskapens hus, Svante Arrhenius väg 14, digitally via conference (Zoom), public link https://stockholmuniversity.zoom.us/j/62786621027, Stockholm, 13:00 (English)
Opponent
Supervisors
Funder
Swedish Research Council, 1359211
Note

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Manuscript. Paper 4: Manuscript.

Available from: 2020-08-26 Created: 2020-07-20 Last updated: 2022-02-26Bibliographically approved

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Lindgren, AmelieLu, ZhengyaoZhang, QiongHugelius, Gustaf

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