Summary
Deep ice cores from ice sheets are unique climate archives. Chemical impurities, deposited initially on the ice sheets, are used to reconstruct the climate of the past. Despite being investigated for several decades, the processes affecting impurities after their deposition, i.e. in the microstructure of ice, still need to be clarified, especially in the oldest parts of the cores. While the quest for the oldest ice in Antarctica has just started, many open questions remain before analysing ice up to 1.5 Myr old. The MESMERISE project aims at investigating the oldest sections of the NGRIP, NEEM, and RECAP ice cores from Greenland, which reach back to the last interglacial, the Eemian, and thus enable important insights into the conditions of a warmer world. In NGRIP ice, annual layers and impurities seem unaffected by relocation, diffusion, and deformational processes. MESMERISE will test this hypothesis by comparing chemical impurities in Holocene and Eemian ice using two state-of-the-art laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) 2D Imaging systems. The recent refinement of the LA-ICP-MS technique by Assist. Prof. Pascal Bohleber at the University of Venice enables the analysis of impurities in ice in ultra-high resolution and two dimensions. It is thus the most promising tool to distinguish annual layers and the localisation of different chemical elements in the microstructure of deep polar ice to investigate post-depositional processes affecting the climate signal. Assist. Prof. Bohleber and Assoc. Prof Anders Svensson, an ice core analysis expert from the Center for Physics of Ice, Climate and Earth at the University of Copenhagen, Denmark, guarantee high-quality research and education at the host institutions. MESMERISE will further build a bridge between the two ERC projects AiCE and Green2Ice, and thus benefits from possibilities such as applying machine learning and modelling to experimental and observational data.
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| Web resources: | https://cordis.europa.eu/project/id/101146092 |
| Start date: | 01-04-2025 |
| End date: | 31-03-2027 |
| Total budget - Public funding: | - 172 750,00 Euro |
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Original description
Deep ice cores from ice sheets are unique climate archives. Chemical impurities, deposited initially on the ice sheets, are used to reconstruct the climate of the past. Despite being investigated for several decades, the processes affecting impurities after their deposition, i.e. in the microstructure of ice, still need to be clarified, especially in the oldest parts of the cores. While the quest for the oldest ice in Antarctica has just started, many open questions remain before analysing ice up to 1.5 Myr old. The MESMERISE project aims at investigating the oldest sections of the NGRIP, NEEM, and RECAP ice cores from Greenland, which reach back to the last interglacial, the Eemian, and thus enable important insights into the conditions of a warmer world. In NGRIP ice, annual layers and impurities seem unaffected by relocation, diffusion, and deformational processes. MESMERISE will test this hypothesis by comparing chemical impurities in Holocene and Eemian ice using two state-of-the-art laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) 2D Imaging systems. The recent refinement of the LA-ICP-MS technique by Assist. Prof. Pascal Bohleber at the University of Venice enables the analysis of impurities in ice in ultra-high resolution and two dimensions. It is thus the most promising tool to distinguish annual layers and the localisation of different chemical elements in the microstructure of deep polar ice to investigate post-depositional processes affecting the climate signal. Assist. Prof. Bohleber and Assoc. Prof Anders Svensson, an ice core analysis expert from the Center for Physics of Ice, Climate and Earth at the University of Copenhagen, Denmark, guarantee high-quality research and education at the host institutions. MESMERISE will further build a bridge between the two ERC projects AiCE and Green2Ice, and thus benefits from possibilities such as applying machine learning and modelling to experimental and observational data.Status
SIGNEDCall topic
HORIZON-MSCA-2023-PF-01-01Update Date
02-10-2024
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