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Conservation Research Institute

 
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UCCRI is an Interdisciplinary Research Centre, with a network of over 150 researchers from all 6 Schools of the University of Cambridge. The Institute supports multidisciplinary research on biodiversity conservation and the social context within which humans engage with nature. It works from a base in the David Attenborough Building, which is designed to enhance collaboration and the sharing of perspectives across organisational and disciplinary boundaries.
Updated: 45 min 27 sec ago

Wed 12 Mar 14:00: Spatio-temporal Melt and Basal Channel Evolution on Pine Island Glacier Ice Shelf from CryoSat-2

Wed, 05/03/2025 - 10:58
Spatio-temporal Melt and Basal Channel Evolution on Pine Island Glacier Ice Shelf from CryoSat-2

Ice shelves buttress the grounded ice sheet, restraining its flow into the ocean. Mass loss from these ice shelves occurs primarily through ocean-induced basal melting, with the highest melt rates occurring in regions that host basal channels – elongated, kilometre-wide zones of relatively thin ice. While some models suggest that basal channels could mitigate overall ice shelf melt rates, channels have also been linked to basal and surface crevassing, leaving their cumulative impact on ice-shelf stability uncertain. Due to their relatively small spatial scale and the limitations of previous satellite datasets, our understanding of how channelised melting evolves over time remains limited. In this study, we present a novel approach that uses CryoSat-2 radar altimetry data to calculate ice shelf basal melt rates, demonstrated here as a case study over Pine Island Glacier (PIG) ice shelf. Our method generates monthly Digital Elevation Models (DEMs) and melt maps with a 250 m spatial resolution. The data show that near the grounding line, basal melting preferentially melts a channel’s western flank 50% more than its eastern flank. Additionally, we find that the main channelised geometries on PIG are inherited upstream of the grounding line and play a role in forming ice shelf pinning points. These observations highlight the importance of channels under ice shelves, emphasising the need to investigate them further and consider their impacts on observations and models that do not resolve them.

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Tue 18 Mar 11:00: Could stratospheric aerosol injection produce meaningful global cooling without novel aircraft? Teams link: https://teams.microsoft.com/l/meetup-join/19%3ameeting_Njk5ZjBhMmUtMmIwMS00YjNkLWE4N2QtOTYwN2EyZGRhMzI5%40thread.v2/0?context...

Wed, 05/03/2025 - 09:52
Could stratospheric aerosol injection produce meaningful global cooling without novel aircraft?

Stratospheric aerosol injection (SAI) is a proposed method of cooling the planet and reducing the impacts of climate change by adding a layer of small particles to the high atmosphere where they would reflect a fraction of incoming sunlight. While it is likely that SAI could reduce global temperature, it has many serious risks and would not perfectly offset climate change. For SAI to be effective, injection would need to take place in the stratosphere. The height of the transition to the stratosphere decreases with latitude, from around 17km near the equator to 8km near the poles. The required injection height would therefore also decrease for higher latitude injection. In this talk, I will present simulations of SAI in an earth system model, UKESM , which quantify how impacts would vary with the injection location and timing, focusing on low-altitude high-latitude injection strategies. Our results suggest that SAI could meaningfully cool the planet even if limited to using existing large jets and injecting at around 13km altitude, if this injection is in the high latitudes during spring and summer. However, relative to a more optimal deployment with novel aircraft at 20km, this strategy requires three times more sulphur dioxide injection and so would strongly increase some side-effects.

Teams link: https://teams.microsoft.com/l/meetup-join/19%3ameeting_Njk5ZjBhMmUtMmIwMS00YjNkLWE4N2QtOTYwN2EyZGRhMzI5%40thread.v2/0?context=%7b%22Tid%22%3a%2249a50445-bdfa-4b79-ade3-547b4f3986e9%22%2c%22Oid%22%3a%2253b919d9-f8a7-4f56-9bb0-baaf0ba7404d%22%7d

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