Hosted by the Institute of Computing for Climate Science
Recordings of recent meetings
The cross-VESRI monthly journal club is held on the second Tuesday of each month, hosted virtually by ICCS. Members of the VESRI teams and ICCS present research papers or concepts that have been significant to their research and are of interest to the other teams. This is an opportunity for researchers to see the cross-cutting work that is being done across the VESRIs, and draw connections with their own work.
Recordings of the journal club are taken and posted online on our Youtube page; recent videos have been embedded below, with the most recent recording first.
January 2024
Introduction to the FETCH4 Project
Presented by Vasilii Petrenko, Alex Turner and Lee Murray
Abstract: FETCH₄: Fate, Emissions, and Transport of CH₄, is an international collaboration of scientists focused on improving our understanding of the past and modern methane cycle, which is a key component of the global carbon cycle. Atmospheric methane concentrations have exhibited large variations over time, and have rapidly grown since 2020, yet the drivers of these variations remain scientifically elusive. The FETCH4 team will collect data from both Greenland ice cores and air samples from stations around the world and measure their unique chemical fingerprints. Their goal is to single out individual aspects of the methane cycle, such as those coming from fossil fuel emissions, and identify both their sources and sinks. Using what they learn, the team will develop and sharpen the capability of global climate models to account for methane. Scientists hope that by creating more efficient models, which will be accelerated by machine learning, they can better interpret these chemical fingerprints and more efficiently capture the methane feedback mechanism in global climate models.
November 2023
Introduction to the CALIPSO Project
Presented by Dr Philippe Ciais, Dr Corinne Le Quére and Dr Daniel Goll
Abstract: This is an introduction to one of the new VESRI teams called CALIPSO (Carbon Loss in Plants, Soils, and Oceans). Led by research teams at the Université de Versailles Saint-Quentin-en-Yvelines, University of East Anglia, and University of Exeter, CALIPSO quantifies vulnerabilities of terrestrial and oceanic carbon stocks under climate change. By integrating novel observations, theoretical understanding, and machine learning tools, the project assesses risks associated with carbon cycle tipping points and provides refined emissions reduction strategies.
October 2023
Software Reproducibility
Presented by Dr Marion Weinzierl, Senior Research Software Engineer Institute of Computing for Climate Science (ICCS).
Abstract: Reproducibility is a fundamental principle of research, and this does not only apply to experimental and empirical research, but also to research relying on computer programs and simulations. In the past decade or so, the so-called replication crisis has led to a number of initiatives and networks which address issues with computational reproducibility. This talk explains what is meant by software/computational reproducibility and why it is important. It showcases a number of initiatives dealing with this topic, and gives guidance on how to improve the reproducibility of your research code.
ICCS is hosting a ReproHack event in March 2024 where individuals or teams try to reproduce scientific findings using code and data that has been openly published. Marion encourages VESRI researchers to submit their papers and codes in order to receive feedback on their reproducibility, and get hands-on experience with software reproducibility.
September 2023
Energy Efficiency in High Performance Computing
Presented by Chris Edsall, Co-director Institute of Computing for Climate Science (ICCS), Head of Software Engineering at the University of Cambridge.
Abstract: At the SuperComputing 2022 conference the Test Of Time Award was presented to Chung-hsing Hsu and Wu Feng for their 2005 paper "A Power-Aware Run-Time System for High Performance Computing". In 2006 they proposed the Green500, an alternative ranking of supercomputers to the Top500 that measured not only their raw compute power but also the energy used while running the benchmark. Current HPC systems can consume significant amounts of electrical energy. For example, the current number one system, while being near the state of the art for energy efficiency, still requires 22 MegaWatts of power to provide its 1.1 ExaFLOPs. As in most cases energy generation results in emission of CO2 , HPC community should consider its carbon impact. This talk will look at some of the considerations of energy efficient supercomputing.
August 2023
How sea-ice rheology should be considered as a subgridscale parameterisation of stress relaxation with the ice
Presented by Dr Einar Ólason, Research Leader at the Nansen Environmental and Remote Sensing Center (SASIP)
Abstract: Sea ice is a crucial component of the earth system and a key component in climate and earth system models. Sea ice is a highly effective insulator, so there is very little heat exchange between the ocean and atmosphere possible through sea ice and no moisture or gas exchange where the ocean is ice covered. But the ice is neither homogenous nor stationary; it drifts under the influence of wind and ocean currents, and for it to move, it first has to break. The cracks that form can let massive amounts of heat, moisture and gasses escape the ocean into the atmosphere, impacting both it and the sea below. In this talk, I will explore some intriguing properties of ice drift and deformation and relate them to how the ice breaks and how we can model this breaking with so-called brittle rheologies. We will see how we can use the concepts of fractures propagating from small to large spatial scales to inform our choices of how to model the internal ice stress in the ice. I will argue that sea-ice rheology should be considered a sub-grid-scale parameterisation of stress relaxation to be consistent with the observed spatial scaling of sea-ice deformation. The group behind the SASIP project has been working on implementing these ideas in a large-scale sea-ice model, and I will conclude by showing some of our latest results with the neXtSIM model.
June 2023
Lightweight code verification for science
Presented by Dr Dominic Orchard, Institute of Computing for Climate Science (ICCS), University of Cambridge
Abstract: This journal club talk will give an overview of two papers "A computational science agenda for programming language research" (Orchard, Rice, 2014) and "Evolving Fortran types with inferred units-of-measure" (Orchard, Rice, Oshmyan, 2015) which represents some of my earlier work looking at how programming languages, tools, and systems could be developed to support the work of computational scientists. I'll go over some principles of how tools can be used to rule out bugs in code, in particular looking at the CamFort suite of tools which we developed for scientists working with Fortran. I'll show how you could deploy CamFort in your projects to gain greater assurance and speed up development practices, and also give an idea of other available tools that you might like to consider using in other languages.
May 2023
What is Bayesian optimization and can we use it for history-matching?
Presented by Dr Henry Moss, Institute of Computing for Climate Science (ICCS), University of Cambridge
Abstract: In this month's edition of the journal club, I will be discussing Bayesian optimization. This powerful machine learning method for calibrating expensive complicated systems is a very active area of research in the ML community and has numerous successful applications across science and industry. Recently, very similar methods have been deployed to help calibrate large climate models (e.g. history matching in DataWave). After covering some core concepts, I will stress the link between Bayesian optimization and history matching. Finally, I will provide a high-level overview of recent progress in Bayesian optimization and some initial thoughts on how we could apply these ideas to help ease climate model calibration.
March 2023
Warming of Global Abyssal and Deep Southern Ocean Waters between the 1990s and 2000s: Contributions to Global Heat and Sea Level Rise Budgets
Presented by Dr Laura Cimoli, Institute of Computing for Climate Science (ICCS), University of Cambridge
February 2023
Machine Learning to Parametrise Convection in Climate Models
Presented by Simon Driscoll, University of Reading
December 2022
The challenges (and opportunities) in using ML for climate and turbulence modelling
Presented by Pedram Hassanzadeh, Rice University
The challenges and opportunities in reconciling balloon observations with gravity wave parameterisations
Presented by Francois Lott, LMD, Paris