Multifractal Analysis for Evaluating the Representation of Clouds in Global Kilometre-Scale Models
(2024)
Postprocessing East African rainfall forecasts using a generative machine learning model
(2024)
A Machine Learning Approach for Predicting Essentiality of Metabolic Genes
In: Braman, J.C. (eds) Synthetic Biology. Methods in Molecular Biology, vol 2760 (2024)
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.
Predictable decadal forcing of the North Atlantic jet speed by sub-polar North Atlantic sea surface temperatures
Weather and Climate Dynamics Copernicus Publications 4:4 (2023) 853-874
Abstract:
It has been demonstrated that decadal variations in the North Atlantic Oscillation (NAO) can be predicted by current forecast models. While Atlantic Multidecadal Variability (AMV) in sea surface temperatures (SSTs) has been hypothesised as the source of this skill, the validity of this hypothesis and the pathways involved remain unclear. We show, using reanalysis and data from two forecast models, that the decadal predictability of the NAO can be entirely accounted for by the predictability of decadal variations in the speed of the North Atlantic eddy-driven jet, with no predictability of decadal variations in the jet latitude. The sub-polar North Atlantic (SPNA) is identified as the only obvious common source of an SST-based signal across the models and reanalysis, and the predictability of the jet speed is shown to be consistent with a forcing from the SPNA visible already within a single season. The pathway is argued to be tropospheric in nature, with the SPNA-associated heating extending up to the mid-troposphere, which alters the meridional temperature gradient around the climatological jet core. The relative roles of anthropogenic aerosol emissions and the Atlantic Meridional Overturning Circulation (AMOC) at generating predictable SPNA variability are also discussed. The analysis is extensively supported by the novel use of a set of seasonal hindcasts spanning the 20th century and forced with prescribed SSTs.Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model
Geoscientific Model Development Copernicus Publications 16:15 (2023) 4501-4519