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Long term hydrological change, the El Niño/Southern Oscillation and biomass burning in the tropics

Rapidly rising levels of atmospheric greenhouse gases including carbon dioxide and methane since the industrial revolution have drawn scientific attention to the importance of the global carbon cycle to the climate (Cubasch et al., 2013). Tropical peatlands, the majority of which are located in the Indonesian region, are a major source of uncertainty in the carbon cycle as the relationships between carbon accumulation and hydrological changes remain poorly understood (Hergoualc’h & Verchot, 2011, Page et al., 2011). An important driver of carbon emissions in tropical peatlands is fire, which in the Indonesian region is strongly influenced on interannual timescales by the El Niño/Southern Oscillation (ENSO). However, it is not clear how ENSO and fire have varied at decadal to centennial scales over the past two millennia. This thesis explores long term tropical hydrological variability and ENSO-like climate change from palaeorecords and their interactions with fire. Using a wide range of instrumental, proxy and model datasets and a novel reconstruction method, two separate reconstructions of long-term ENSO-like climate change are produced based on precipitation and temperature data. These show no evidence of a difference between the ENSO-like behaviour of precipitation and temperature. There is limited evidence for a difference in long-term ENSO-like state between the Medieval Climate Anomaly and the Little Ice Age. Reconstructions of hydrological variability and biomass burning in the Indonesian region suggest that precipitation and fire have been positively correlated over the past 2,000 years, which is contrary to the modern-day relationship on ENSO timescales. This throws up questions of long-term versus short-term interactions and feedbacks between fire, climate and vegetation. It is likely that anthropogenic activity in the Indonesian region has significantly altered the stability of the fire regime. Further research combining proxy data, climate and fire models, and using more robust statistical analysis is necessary to untangle the natural and anthropogenic driving factors at different time resolutions.

Identiferoai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:716805
Date January 2016
CreatorsHenke, Lilo Maria Keti
ContributorsCharman, Dan
PublisherUniversity of Exeter
Source SetsEthos UK
Detected LanguageEnglish
TypeElectronic Thesis or Dissertation
Sourcehttp://hdl.handle.net/10871/27975

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