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dc.contributor.advisorRypdal, Martin
dc.contributor.authorBochow, Nils
dc.date.accessioned2024-06-04T12:27:25Z
dc.date.available2024-06-04T12:27:25Z
dc.date.issued2024-06-17
dc.description.abstractClimate change can trigger climate tipping points, which are among the major threats to human society. Tipping points are thresholds beyond which a system undergoes abrupt, often irreversible, changes even if the external forcing is brought to a halt. Several large-scale elements in the Earth system are considered tipping elements with global consequences once critical thresholds are crossed and self-reinforcing changes are triggered. However, there is a large uncertainty as to whether some Earth system components should be considered tipping elements. The precise values of the critical thresholds remain uncertain, and it is unclear whether these can be temporarily exceeded without triggering a tipping point. Moreover, incomplete historical records complicate the inference of past dynamics of these components and current reconstruction methods introduce biases into higher-order statistics that are used to assess their stability. On the other hand, with the increasing availability of data and advancements in computational power, deep learning (DL) offers new advances in climate science, ranging from reconstructions to hybrid climate models. This thesis presents an in-depth study of two distinct tipping elements: the Greenland ice sheet (GrIS) and the coupled system of the South American Monsoon and the Amazon rainforest (SAMS). Furthermore, we introduce a novel deep learning-based method to reconstruct spatiotemporal climate fields. By combining model- and observation-based analyses, we show that the SAMS is approaching a critical transition in response to deforestation, potentially leading to a large-scale reduction in precipitation rates in large parts of South America. We associate the critical transition with a weakening of the oceanic moisture inflow due to forest degradation. Subsequently, we use two independent ice-sheet models and show for the first time that the GrIS's critical threshold can be temporarily exceeded without prompting a transition to an alternative state. Timely reversal of surface temperatures can prevent a complete retreat of the ice sheet due to the slow timescale of the ice loss. Lastly, we present a new deep learning-based reconstruction method. The model learns the underlying spatial relationships from climate model output and can inpaint observation-based datasets. Our method outperforms previous reconstruction methods and can realistically reconstruct known historical events, highlighting the potential of DL.en_US
dc.description.doctoraltypeph.d.en_US
dc.description.popularabstractIn recent years, the concept of tipping elements has emerged as a key topic in the discourse on climate change and its impacts. Tipping elements are large-scale elements in the Earth’s system that could undergo abrupt and often irreversible changes once a critical threshold, called tipping point, is exceeded. In this thesis, we investigate two distinct tipping elements: the coupled system of the South American monsoon-Amazon rainforest (SAMS) and the Greenland ice sheet (GrIS). Additionally, we introduce a new machine learning method to reconstruct historical spatiotemporal climate fields. Guided by numerical modeling results that we carry out, we identify physical and statistical evidence in observational data that the SAMS is nearing a critical transition, with substantial consequences for the climate in large parts of South America. Furthermore, using two independent ice sheet models, we show that the critical threshold of the GrIS may be temporarily exceeded without prompting a large-scale loss of the ice sheet, provided temperatures are subsequently reduced below the threshold. This finding might have implications for other slow tipping elements and lays the basis for a more realistic treatment of tipping elements. Additionally, we show that our machine learning method for climate field reconstruction and gap filling outperforms existing methods and has the potential to enhance our understanding of the stability of several tipping elements by providing consistent long-term time series.en_US
dc.identifier.isbn978-82-8236-570-3 (trykt) / 978-82-8236-571-0 (pdf)
dc.identifier.urihttps://hdl.handle.net/10037/33717
dc.language.isoengen_US
dc.publisherUiT Norges arktiske universiteten_US
dc.publisherUiT The Arctic University of Norwayen_US
dc.relation.haspart<p>Paper I: Bochow, N. & Boers, N. (2023). The South American monsoon approaches a critical transition in response to deforestation. <i>Science Advances, 9</i>(40), eadd9973. Also available in Munin at <a href=https://hdl.handle.net/10037/31582>https://hdl.handle.net/10037/31582</a>. <p>Paper II: Bochow, N., Poltronieri, A., Robinson, A., Montoya, M., Rypdal, M. & Boers, N. (2023). Overshooting the critical threshold for the Greenland ice sheet. <i>Nature, 622</i>, 528–536. Also available in Munin at <a href=https://hdl.handle.net/10037/31590> https://hdl.handle.net/10037/31590</a>. <p>Paper III: Bochow, N., Poltronieri, A., Rypdal, M. & Boers, N. Reconstructing historical climate fields with deep learning. (Manuscript). Also available on arXiv at <a href=https://doi.org/10.48550/arXiv.2311.18348>https://doi.org/10.48550/arXiv.2311.18348</a>.en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2024 The Author(s)
dc.subject.courseIDDOKTOR-004
dc.subjectTipping Pointsen_US
dc.subjectGreenland ice sheeten_US
dc.subjectSouth American Monsoon Systemen_US
dc.subjectMachine Learningen_US
dc.subjectEarth System Modellingen_US
dc.titleModelling the Earth System - From Tipping Elements to Reconstructionsen_US
dc.typeDoctoral thesisen_US
dc.typeDoktorgradsavhandlingen_US


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