Mechanistically understanding biomineralisation and ancient ocean chemistry changes to facilitate robust climate model validation
Mechanistically understanding biomineralisation and ancient ocean chemistry changes to facilitate robust climate model validation
批准号:
EP/Y034252/1
负责人:
David Evans
金额:
$222.77万
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --
中文摘要
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英文摘要
The study of warm intervals in Earth's geologic past with naturally higher-than-modern atmospheric CO2 is of crucial societal importance because this information provides: i) a way of determining how much temperature is likely to rise as we burn fossil fuels, and ii) an empirical means of assessing the quality of state-of-the-art climate models. Most past climate information comes from 'proxies', which relate small changes in the chemical composition of fossil materials to their environment of formation, of which the shells of the foraminifera (unicellular marine organisms) possibly represent the most widely used archive. Unfortunately, no proxy system is perfect, in that they are all responsive to more than one factor. For example, paleothermometers based on the geochemistry of foraminifera are sensitive to both the temperature and chemistry of the seawater they formed in, the latter of which has changed through time in a poorly understood way. In addition, there is no consensus view on even the basic mechanisms involved in the process by which foraminifera build their shell, such that we don't know when proxy reconstructions are likely to be biased. AMOEBA will directly tackle both issues by making extremely novel observations of living foraminifera to determine how they form their shell, and by generating the necessary records of past chemical change in the oceans. This will facilitate a step-change in the interpretationof quantitative proxy data, transforming the utility of such datasets. AMOEBA will apply this knowledge to a new Cenozoic data compilation, producing accurate estimates of Earth's temperature during key greenhouse intervals. Thus, by facilitating the next generation of model-data comparison, the ability of state-of-the-art climate models to capture key features of greenhouse warmth will be unambiguously assessed for the first time.
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