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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.
URL: http://talks.cam.ac.uk/show/index/59874
Updated: 36 min 17 sec ago

Wed 12 Feb 13:00: Short-term, high-resolution sea ice forecasting with diffusion model ensembles

Mon, 10/02/2025 - 15:27
Short-term, high-resolution sea ice forecasting with diffusion model ensembles

Sea ice plays a key role in Earth’s climate system and exhibits significant seasonal variability as it advances and retreats across the Arctic and Antarctic every year. The production of sea ice forecasts provides great scientific and practical value to stakeholders across the polar regions, informing shipping, conservation, logistics, and the daily lives of inhabitants of local communities. Machine learning offers a promising means by which to develop such forecasts, capturing the nonlinear dynamics and subtle spatiotemporal patterns at play as effectively—if not more effectively—than conventional physics-based models. In particular, the ability of deep generative models to produce probabilistic forecasts which acknowledge the inherent stochasticity of sea ice processes and represent uncertainty by design make them a sensible choice for the task of sea ice forecasting. Diffusion models, a class of deep generative models, present a strong option given their state-of-the-art performance on computer vision tasks and their strong track record when adapted to spatiotemporal modelling tasks in weather and climate domains. In this talk, I will present preliminary results from a IceNet-like [1] diffusion model trained to autoregressively forecast daily, 6.25 km resolution sea ice concentration in the Bellingshausen Sea along the Antarctic Peninsula. I will also touch on the downstream applications for these forecasts, from conservation to marine route planning, which are under development at the British Antarctic Survey (BAS). I welcome ideas and suggestions for improvement and look forward to discussing opportunities for collaboration within and beyond BAS .

[1] Andersson, Tom R., et al. “Seasonal Arctic sea ice forecasting with probabilistic deep learning.” Nature communications 12.1 (2021): 5124. https://www.nature.com/articles/s41467-021-25257-4

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Fri 21 Feb 17:30: Eve's Byte of the Apple

Sun, 09/02/2025 - 12:42
Eve's Byte of the Apple

Abstract:

In “Eve’s Byte of the Apple”, Sandi Toksvig will be taking an alternative look at the evolution of information, at how the knowledge of women and about women is encoded, and what comes from those codes. Since 2023 Sandi has been a Bye-Fellow at Christ’s College, Cambridge working on The Mappa Mundi Project, creating a global interactive digital platform telling women’s stories worldwide. In this lecture, she considers how the evolution of information technology has been historically biased against women, continuing that bias to the present day. Most importantly, she asks what might be done about it.

Biography:

Sandi Toksvig was born in Copenhagen, Denmark but grew up travelling the world. After graduating with a first-class degree from Cambridge, Sandi began a career on stage, television and radio. As a political and women’s rights activist, she was co-founder of the Women’s Equality Party in 2015. Sandi has written stage plays, journalism and over 25 books including fact and fiction for both children and adults. Her latest novel Friends of Dorothy was published in 2024.

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Wed 12 Feb 14:00: Short-term, high-resolution sea ice forecasting with diffusion model ensembles

Wed, 05/02/2025 - 11:44
Short-term, high-resolution sea ice forecasting with diffusion model ensembles

Sea ice plays a key role in Earth’s climate system and exhibits significant seasonal variability as it advances and retreats across the Arctic and Antarctic every year. The production of sea ice forecasts provides great scientific and practical value to stakeholders across the polar regions, informing shipping, conservation, logistics, and the daily lives of inhabitants of local communities. Machine learning offers a promising means by which to develop such forecasts, capturing the nonlinear dynamics and subtle spatiotemporal patterns at play as effectively—if not more effectively—than conventional physics-based models. In particular, the ability of deep generative models to produce probabilistic forecasts which acknowledge the inherent stochasticity of sea ice processes and represent uncertainty by design make them a sensible choice for the task of sea ice forecasting. Diffusion models, a class of deep generative models, present a strong option given their state-of-the-art performance on computer vision tasks and their strong track record when adapted to spatiotemporal modelling tasks in weather and climate domains. In this talk, I will present preliminary results from a IceNet-like [1] diffusion model trained to autoregressively forecast daily, 6.25 km resolution sea ice concentration in the Bellingshausen Sea along the Antarctic Peninsula. I will also touch on the downstream applications for these forecasts, from conservation to marine route planning, which are under development at the British Antarctic Survey (BAS). I welcome ideas and suggestions for improvement and look forward to discussing opportunities for collaboration within and beyond BAS .

[1] Andersson, Tom R., et al. “Seasonal Arctic sea ice forecasting with probabilistic deep learning.” Nature communications 12.1 (2021): 5124. https://www.nature.com/articles/s41467-021-25257-4

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Wed 19 Mar 14:00: Cell edges: from polarity to growth control Please contact the events team at Events@slcu.cam.ac.uk for the online Zoom link.

Tue, 04/02/2025 - 15:43
Cell edges: from polarity to growth control

Abstract not available

Please contact the events team at Events@slcu.cam.ac.uk for the online Zoom link.

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Tue 04 Feb 11:00: Planetary uprising: Climate colonialism, Extinction Rebellion and the transformation of global politics Teams link: https://teams.microsoft.com/l/meetup-join/19%3ameeting_Y2VlZmM3OTgtOTQwNS00ZTcxLTk5ZGEtZWZiMzU4NTdiMGY1%40thread.v2/0...

Mon, 03/02/2025 - 10:42
Planetary uprising: Climate colonialism, Extinction Rebellion and the transformation of global politics

Dear all,

CAS seminar will welcome Tobias Müller who will give us a talk on climate colonialism. The talk will be held in a hybrid format with the speaker in-person at the Unilever lecture theatre and on Zoom on Tuesday, the 4th February , 11 AM-12 PM. Please find the abstracts of the talk below.

If you would like this seminar recorded, please let us know in advance. We look forward to seeing you there!

Best wishes, Megan and Yao

—————————————

Leverhulme Early Career Fellow Centre for Research in the Arts, Social Sciences and Humanities (CRASSH) The climate crisis is deeply entangled with the politics of race and colonialism. The concept of “climate colonialism”, (Bhambra and Newell 2022) urges us to analyse what forms of resistance to the socio-ecological continuities of colonialism emerge, and what challenges they face. However, we lack empirical and conceptual studies on how people on the ground confront the intersection of the climate crisis, colonialism, racism and extractivism, and how this differs across former coloniser and colonised countries. This raises the question, what kind of politics are able to confront the intersecting crises of climate and colonialism?

This presentation addresses this gap through an analysis of how climate activists in four different countries respond to the climate crisis and connected social justice issues. Using the case study of a transnationally operating group within the global movement, Extinction Rebellion, the paper compares strategic responses to climate colonialism in four different countries, namely Mexico, South Africa, the UK and the US. Methodologically, the paper uses multi-sited ethnography, comprising 18 months of ethnographic fieldwork and 140 interviews with activists, to gain a deep insight into the internal contentions within different parts of the movement.The paper advances not only our understanding of how facing climate colonialism challenges movement spaces, but also how white environmental activists struggle with building racial justice into their practices and to build coalitions across the social justice movement space. It thereby contributes to the much-needed bridging between decolonial theory, social movement studies and the social scientific accounts of climate change.

Teams link: https://teams.microsoft.com/l/meetup-join/19%3ameeting_Y2VlZmM3OTgtOTQwNS00ZTcxLTk5ZGEtZWZiMzU4NTdiMGY1%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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Wed 26 Feb 15:30: Title to be confirmed

Mon, 03/02/2025 - 09:27
Title to be confirmed

Abstract not available

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Wed 23 Apr 14:00: Title to be confirmed

Mon, 03/02/2025 - 09:25
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Abstract not available

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Wed 09 Apr 14:00: Title to be confirmed

Mon, 03/02/2025 - 09:24
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Wed 26 Mar 14:00: Title to be confirmed

Mon, 03/02/2025 - 09:24
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Wed 12 Mar 14:00: Title to be confirmed

Mon, 03/02/2025 - 09:23
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Fri 14 Feb 16:00: Synchronization in Navier-Stokes turbulence and it's role in data-driven modeling

Sat, 01/02/2025 - 10:27
Synchronization in Navier-Stokes turbulence and it's role in data-driven modeling

n Navier-Stokes (NS) turbulence, large-scale turbulent flows determine small-scale flows; in other words, small-scale flows are synchronized to large-scale flows. In 3D turbulence, previous numerical studies suggest that the critical length separating these two scales is determined by the Kolmogorov length. In this talk, I will introduce our theoretical framework for characterizing synchronization phenomena [1]. Specifically, it provides a computational method for the exponential rate of convergence to the synchronized state, and identifies the critical length based on the NS equations via the “transverse” Lyapunov exponent. I will also discuss the synchronization property of 2D NS turbulence and how it differs from the 3D case [2]. These insights into synchronization and critical length scales are essential for developing machine-learning closure models for turbulence, in particular their stable reproducibility [3]. Finally, I will illustrate how “generalized” synchronization is crucial for predicting chaotic dynamics [4].

[1] M. Inubushi, Y. Saiki, M. U. Kobayashi, and S. Goto, Characterizing small-scale dynamics of Navier-Stokes turbulence with transverse Lyapunov exponents: A data assimilation approach, Phys. Rev. Lett. 131, 254001 (2023).

[2] M. Inubushi and C. P. Caulfield (in preparation).

[3] S. Matsumoto, M. Inubushi, and S. Goto, Stable reproducibility of turbulence dynamics by machine learning, Phys. Rev. Fluids 9, 104601 (2024).

[4] A. Ohkubo and M. Inubushi, Reservoir computing with generalized readout based on generalized synchronization, Sci. Rep. 14, 30918 (2024).

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Wed 29 Jan 14:00: Modelling sea ice dynamics using brittle dynamics: impact in pack ice and marginal ice zones

Wed, 29/01/2025 - 11:02
Modelling sea ice dynamics using brittle dynamics: impact in pack ice and marginal ice zones

Sea ice dynamics are highly complex and generally poorly resolved by sea ice models. This is problematic, as they modulate the amount of momentum exchanged between the atmosphere and the ocean in polar regions, as well as play a key role in heat and light fluxes through the opening/closing of sea ice leads. A solution to improve simulated sea ice dynamics is to use a brittle rheology to represent the mechanical behaviour of sea ice. Such rheology is included in the sea ice model neXtSIM, and we demonstrated its ability to capture the observed characteristics and complexity of fine-scale sea ice deformations.Here, we present two cases where we coupled this sea ice model to better understand the role of ice dynamics in ice-ocean interactions.

In the first case, we set up a 12km resolution ocean—sea-ice coupled model, using OPA , the ocean component of NEMO . We investigate the sea ice mass balance of the model for the period 2000-2018. We estimate the contribution of leads and polynyas to winter ice production. We find this contribution to add up from 25% to 35% of the total ice growth in pack ice in winter, showing a significant increase over the 18 years covered by the model simulation.

In the second case, we focus on the marginal ice zone (MIZ) and couple neXtSIM with the wave model WAVEWATCH III . We investigate how wave-induced breakup impacts sea ice dynamics in the MIZ . We show how, using the “damage” quantity that is at the core of the brittle rheology framework, we can represent the loss of ice strength associated with wave-induced breakup, and how breakup can increase the mobility of the thickest ice in the MIZ after storms. For both cases, we will also discuss briefly how using a brittle sea ice model could impact the modelling of Antarctic sea ice using preliminary results from a new configuration.

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Wed 12 Feb 14:00: Short-term, high-resolution sea ice forecasting with diffusion model ensembles

Tue, 28/01/2025 - 09:21
Short-term, high-resolution sea ice forecasting with diffusion model ensembles

Sea ice plays a key role in Earth’s climate system and exhibits significant seasonal variability as it advances and retreats across the Arctic and Antarctic every year. The production of sea ice forecasts provides great scientific and practical value to stakeholders across the polar regions, informing shipping, conservation, logistics, and the daily lives of inhabitants of local communities. Machine learning offers a promising means by which to develop such forecasts, capturing the nonlinear dynamics and subtle spatiotemporal patterns at play as effectively—if not more effectively—than conventional physics-based models. In particular, the ability of deep generative models to produce probabilistic forecasts which acknowledge the inherent stochasticity of sea ice processes and represent uncertainty by design make them a sensible choice for the task of sea ice forecasting. Diffusion models, a class of deep generative models, present a strong option given their state-of-the-art performance on computer vision tasks and their strong track record when adapted to spatiotemporal modelling tasks in weather and climate domains. In this talk, I will present preliminary results from a IceNet-like [1] diffusion model trained to autoregressively forecast daily, 6.25 km resolution sea ice concentration in the Bellingshausen Sea along the Antarctic Peninsula. I will also touch on the downstream applications for these forecasts, from conservation to marine route planning, which are under development at the British Antarctic Survey (BAS). I welcome ideas and suggestions for improvement and look forward to discussing opportunities for collaboration within and beyond BAS .

[1] Andersson, Tom R., et al. “Seasonal Arctic sea ice forecasting with probabilistic deep learning.” Nature communications 12.1 (2021): 5124. https://www.nature.com/articles/s41467-021-25257-4

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Tue 04 Mar 11:00: High-Resolution PM2.5 Mapping Across Malaysia Using Multi-Satellite Data and Machine Learning Techniques https://teams.microsoft.com/l/meetup-join/19%3ameeting_MTQ5N2Q5ZDYtODRmYi00MzJhLTg0ZjctNjc2NGVlZDUzYmUx%40thread.v2/0?context=%7b...

Mon, 27/01/2025 - 22:44
High-Resolution PM2.5 Mapping Across Malaysia Using Multi-Satellite Data and Machine Learning Techniques

Air pollution assessment in urban and rural areas is really challenging due to high spatio-temporal variability of aerosols and pollutants and the uncertainties in measurements and modelling estimates. Nevertheless, accurate determination of the pollution sources and distribution of PM2 .5 concentrations is especially important for source apportionment and mitigation strategies. This study provides estimates of PM2 .5 concentrations across Malaysia in high spatial resolution, based on multi-satellite data and machine learning (ML) models, namely Random Forest (RF), Support Vector Regression (SVR) and extreme Gradient Boosting (XGBoost), also covering remote areas without measurement networks. The study aims to develop ML models that are simpler than previous works and demonstrate computational efficiency. Six sub-models were developed to represent different locations and seasons in Malaysia. Model 1 includes all data from 65 air-quality stations, Models 2 and 3 characterize urban/industrial and suburban sites, respectively, while Models 4 to 6 correspond to dry, wet, and inter-monsoon seasons, respectively. The RF technique exhibited slightly better performance compared to the XGBoost and SVR approaches. More specifically, for model 1, it exhibited a high correlation with a coefficient of determination (R2) of 0.64 and RMSE of 12.17 μg m−3, while similar results were obtained for models 3, model 4 and model 5. The lower performance (R2 = 0.16-0.94) observed in the wet and inter-monsoon seasons is due to fewer numbers of data used in model calibration. Integration of two Aerosol Optical Depth products from the Advanced Himawari Imager and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors together with gases pollutants from Sentinel 5P enabled seamless seasonal PM2 .5 mapping over Malaysia, even for a short period of time. However, usage of data with insufficient information during the model training procedure, and lack of satellite data due to cloud contamination, can limit the PM2 .5 prediction accuracy.

https://teams.microsoft.com/l/meetup-join/19%3ameeting_MTQ5N2Q5ZDYtODRmYi00MzJhLTg0ZjctNjc2NGVlZDUzYmUx%40thread.v2/0?context=%7b%22Tid%22%3a%2249a50445-bdfa-4b79-ade3-547b4f3986e9%22%2c%22Oid%22%3a%228b208bd5-8570-491b-abae-83a85a1ca025%22%7d

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Tue 04 Feb 11:00: Could stratospheric aerosol injection produce meaningful global cooling without novel aircraft? https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZjVmYTU2YmItNmMyZC00NGYzLTllZmMtNGU5OWJiMjlhNDAy%40thread.v2/0?context=%7b%22Tid%22...

Mon, 27/01/2025 - 22:33
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.

https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZjVmYTU2YmItNmMyZC00NGYzLTllZmMtNGU5OWJiMjlhNDAy%40thread.v2/0?context=%7b%22Tid%22%3a%2249a50445-bdfa-4b79-ade3-547b4f3986e9%22%2c%22Oid%22%3a%228b208bd5-8570-491b-abae-83a85a1ca025%22%7d

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Mon 10 Mar 13:00: Ice Shelves: Antarctica’s Gatekeepers

Mon, 27/01/2025 - 15:13
Ice Shelves: Antarctica’s Gatekeepers

Abstract not available

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Thu 20 Mar 19:00: Natural Materials for Musical Instruments Please note the start time, being after the AGM, is approximate.

Sun, 26/01/2025 - 11:50
Natural Materials for Musical Instruments

Immediately following the CNHS AGM , Jim Woodhouse will give a Presidential Address on the various uses of natural materials in the making of traditional musical instruments.

The talk will focus mostly on wood: why instrument makers prefer certain particular types of wood, what it is in the cellular structure that makes these timbers special, and what scope there may be to use alternative materials in the light of climate pressure and CITES restrictions.

Please note the start time, being after the AGM, is approximate.

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Thu 13 Mar 18:45: The Cambridgeshire Bird Club - 1925 to 2025: a century of bird watching

Sun, 26/01/2025 - 11:43
The Cambridgeshire Bird Club - 1925 to 2025: a century of bird watching

The people, the places and the birds, losers and winners – a century of the most remarkable changes.

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Thu 27 Feb 18:45: CNHS Field Studies 2024

Sun, 26/01/2025 - 11:41
CNHS Field Studies 2024

This talk will summarise the various CNHS fieldwork projects during 2024.

Jonathan will talk about plants and fungi, Duncan about moth-trapping.

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Thu 20 Feb 18:45: Great Fen: progress on the peat

Sun, 26/01/2025 - 11:29
Great Fen: progress on the peat

Find out the latest from the Great Fen.

With so much going on at the Fen since the purchase of Speechly’s Farm, it’s never too soon for an update.

With the usual sights and sounds from the Great Fen, including trail camera and drone videos.

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