Climate processes

Research group

Research theme

Sub department

About

Multifractal Analysis for Evaluating the Representation of Clouds in Global Kilometre-Scale Models

(2024)

Authors:

Lilli Johanna Freischem, Philipp Weiss, Hannah Christensen, Philip Stier
More details from the publisher

Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws

ArXiv 2312.11176 (2023)

Authors:

Ning Liu, Yiming Fan, Xianyi Zeng, Milan Klöwer, Lu Zhang, Yue Yu
Details from ArXiV

A Machine Learning Approach for Predicting Essentiality of Metabolic Genes

In: Braman, J.C. (eds) Synthetic Biology. Methods in Molecular Biology, vol 2760 (2024)

Authors:

Lilli J Freischem & Diego A Oyarzún

Abstract:

The identification of essential genes is a key challenge in systems and synthetic biology, particularly for engineering metabolic pathways that convert feedstocks into valuable products. Assessment of gene essentiality at a genome scale requires large and costly growth assays of knockout strains. Here we describe a strategy to predict the essentiality of metabolic genes using binary classification algorithms. The approach combines elements from genome-scale metabolic models, directed graphs, and machine learning into a predictive model that can be trained on small knockout data. We demonstrate the efficacy of this approach using the most complete metabolic model of Escherichia coli and various machine learning algorithms for binary classification.
More details from the publisher

Neural General Circulation Models for Weather and Climate

(2023)

Authors:

Dmitrii Kochkov, Janni Yuval, Ian Langmore, Peter Norgaard, Jamie Smith, Griffin Mooers, Milan Klöwer, James Lottes, Stephan Rasp, Peter Düben, Sam Hatfield, Peter Battaglia, Alvaro Sanchez-Gonzalez, Matthew Willson, Michael P Brenner, Stephan Hoyer
More details from the publisher
Details from ArXiV

Rapid saturation of cloud water adjustments to shipping emissions

Atmospheric Chemistry and Physics European Geosciences Union 23:19 (2023) 12545-12555

Authors:

Peter Manshausen, Duncan Watson-Parris, Matthew W Christensen, Jukka-Pekka Jalkanen, Philip Stier

Abstract:

Human aerosol emissions change cloud properties by providing additional cloud condensation nuclei. This increases cloud droplet numbers, which in turn affects other cloud properties like liquid-water content and ultimately cloud albedo. These adjustments are poorly constrained, making aerosol effects the most uncertain part of anthropogenic climate forcing. Here we show that cloud droplet number and water content react differently to changing emission amounts in shipping exhausts. We use information about ship positions and modeled emission amounts together with reanalysis winds and satellite retrievals of cloud properties. The analysis reveals that cloud droplet numbers respond linearly to emission amount over a large range (1–10 kg h−1) before the response saturates. Liquid water increases in raining clouds, and the anomalies are constant over the emission ranges observed. There is evidence that this independence of emissions is due to compensating effects under drier and more humid conditions, consistent with suppression of rain by enhanced aerosol. This has implications for our understanding of cloud processes and may improve the way clouds are represented in climate models, in particular by changing parameterizations of liquid-water responses to aerosol.
More details from the publisher
Details from ORA
More details