UK scientists have made a fundamental breakthrough in the quest to predict fluctuations in the rotation of the Earth accurately and so the length of the day - potentially opening up new predictions for the effects of climate change. 

A team of scientists, led by Professor Adam Scaife from the University of Exeter, has used state-of-the-art mathematical modeling to show how fluctuations in the length of the day can be predicted more than a year in advance – significantly longer than currently possible. 

The team suggests this long-range forecasting also originates from a new atmospheric source for long-range predictability of weather and climate changes.                                                                                                              

Crucially, the research shows a definitive link between geodesy – or accurately measuring and understanding the shape, size, orientation, and gravity on Earth – and climate prediction. 

Professor Scaife, a climate expert from the University of Exeter’s Mathematics department said: “While the changes in day length are tiny, they are important for applications that require very accurate time measurements like GPS.” 

Angular momentum has long been known to play a fundamental role in the structure and variability of the Earth’s atmosphere. 

As the Earth spins around its axis, its overall mass and rotation result in what appears to be a steady rotation. However, surface wind changes and changes in high and low-pressure patterns can change this and if the atmosphere speeds up due to stronger winds, the Earth’s rotation consequently slows down, causing the length of day to increase.   

However, until now the long-range predictability of these fluctuations in the length of the day was unknown. 

The new study shows that fluctuations in atmospheric angular momentum and the length of day are predictable out to more than a year ahead and that the atmospheric changes have an important influence on regional weather and climate.  

Using a range of forecasts from a dynamical climate model, the scientists could predict signals in the atmosphere that spread slowly and coherently towards the poles.  

These signals precede changes in extratropical climate via the North Atlantic Oscillation and the extratropical jet stream. These new findings point to a source of long-range predictability from within the atmosphere that will help us to understand and better predict weather and climate. 

Professor Scaife added: “We usually look to the ocean for long-range prediction signals but these new results show that long-range forecasts can also be driven from within the atmosphere.” 

Rubisco (Ribulose-1,5-bisphosphate carboxylase/oxygenase) is responsible for providing carbon for almost all life on Earth. Rubisco functions by converting atmospheric CO2 from the Earth’s atmosphere to organic carbon matter, which is essential to sustain most life on Earth. 

For some time now, natural variation has been observed among Rubisco proteins of land plants and modeling studies have shown that transplanting Rubisco proteins with certain functional properties can increase the amount of atmospheric CO2 crop plants can uptake and store.

Study lead author, Wasim Iqbal, a Ph.D. researcher at Newcastle University’s School of Natural and Environmental Sciences, part of Dr. Maxim Kapralov’s group, developed a machine learning tool that can predict the performance properties of numerous land plant Rubisco proteins with surprisingly good accuracy. The hope is that this tool will enable the hunt for a ‘supercharged’ Rubisco protein that can be bioengineered into major crops such as wheat.

Published in the Journal Of Experimental Botany, the study presents a useful tool for screening and predicting plant Rubisco kinetics for engineering efforts as well as for fundamental studies on Rubisco evolution and adaptation. Screening the natural diversity of Rubisco kinetics is the main strategy used to find better Rubiscos for crop engineering efforts.

Improving the accuracy of global photosynthesis estimates

Wasim said: “Our study will have huge implications for climate models and bioengineering crops.

“This study provides plant biologists with a pre-screening tool for highlighting Rubisco species exhibiting better kinetics for crop engineering efforts.

“The machine learning tool can be used to improve the accuracy of global photosynthesis estimates. The Rubisco performance properties our model predicts are compatible with Earth system models (ESM) used by climate scientists. Currently, ESMs use a single set of Rubisco properties from the same species (or sometimes a handful) for estimating photosynthesis at the ecosystem scale. Our machine learning tool could provide predictions for most land plants improving the accuracy of ESMs.”

The next steps of this work include isolating the best Rubisco proteins identified from predictions in the lab and attempting to bioengineer a plant species with a foreign Rubisco protein.

University of Nottingham scientists have used machine learning to find new ways to identify and pinpoint diseases in poultry farms, which will help to reduce the need for antibiotic treatment, lowering the risk of antibiotic resistance transferring to human populations.

The study was led by Dr. Tania Dottorini from the School of Veterinary Medicine and Science and Future Food Beacon at the University of Nottingham. The research is part of the FARMWATCH project, a £1.5m partnership between the University and the China National Center for Food Safety Risk Assessment.

The rapid increase in poultry production to meet growing demand in China has resulted in the extensive and indiscriminate use of antibiotics. This has led to a worrying increase in cases of antimicrobial resistance (AMR) diagnosed in animals which could potentially spread to humans, via direct contact, environmental contamination, and food consumption.

With antibiotic resistance now one of the most threatening issues worldwide, effective and rapid diagnostics of bacterial infection in chicken farming can reduce the need for antibiotics, which will reduce epidemics and AMR.

In this project, researchers in Nottingham collected samples from the animals, humans, and environment in a Chinese farm and connected slaughterhouse. This complex ‘big’ data has now been analyzed for new diagnostic biomarkers that will predict and detect a bacterial infection, the insurgence of AMR, and transfer to humans. This data will then allow early intervention and treatment, reducing the spread and the need for antibiotics.

The study produced three key findings. Firstly, several similar clinically relevant antimicrobial resistance genes (ARGs) and associated mobile genetic elements (antibiotic resistance genes able to move within genomes and between bacteria), were found in both human and broiler chicken samples. In particular, eleven types of clinically important antibiotic resistance genes, with conserved mobile ARG gene structures were found between samples from different hosts.

Dr Dottorini said: “These similarities would have been missed if we only used large-scale conventional comparative analysis, which, in fact, showed that microbiome and resistomes differ across environments and hosts. Overall, this finding suggests the relevance of adopting a multi-scale analysis when dissecting similarities and differences of resistomes and microbiomes in complex interconnected environments.”

Secondly, the study showed that by developing a machine learning-powered approach integrating metagenomics data with culture-based methods, the team found the existence of a core chicken gut resistome that is correlated with the AMR circulating in the farms. These results supported the hypothesis that correlations exist between resistance phenotypes of individual commensal and pathogenic bacteria and the types of ARGs in the resistome in which they exist in.

Finally, using sensing technology and machine learning, the team uncovered that the AMR-related core resistome is associated with various external factors such as temperature and humidity.

Dr. Dottorini said: “The food production industry represents a major consumer of antibiotics, but the AMR risks within these environments are still not fully understood. It is therefore critical to set out studies and improved methods optimized to these environments where animals and humans may be in close contact. Precision farming, cost-effective DNA sequencing, and the increased adoption of machine learning technologies offer the opportunity to develop methods giving a better understanding and quantification of AMR risks in farming environments.”

New supercomputer simulations show that truly Earth-like exoplanets with oceans and continents, and beaches along the boundaries, may be much more common around red dwarfs than previously expected. This means ongoing and future exoplanet survey missions can expect to find multiple Earth-analogs for further study before the end of the decade.

The “habitable zone” is defined as the range of orbits around a star where the temperature would be right for an exoplanet to have liquid water on its surface. This doesn’t necessarily mean that there is life or even water on the planet. In fact, for most exoplanets in the habitable zone, life on the planet would be “no day at the beach.” On Earth, both the oceans and the continents play vital roles in the geochemical carbon cycle which helps maintain a temperate climate where liquid water and life can exist. So to look for potentially habitable Earth-like planets, exactly what we need is “a day at the beach,” where the land and sea can coexist.

Previous research had warned that such beach-friendly planets could be extremely rare, even in the habitable zones around the most common types of stars (namely red dwarfs). This is because there is a distinct difference in the water content of rocky materials found in the inner and outer parts of a protoplanetary disk where planets form, leading to the formation of planets with either too much or too little water in most cases. But new numerical simulations conducted by Tadahiro Kimura from the University of Tokyo and Masahiro Ikoma from the National Astronomical Observatory of Japan provide a sunnier view. By taking into consideration water produced from interactions between the still molten surface of a young planet and its primordial atmosphere, the team found that a wide range in final water content is expected. And within that range, several percent of roughly-Earth-sized planets in habitable zones should have appropriate amounts of water for a temperate climate. This is a high enough percentage that ongoing and future exoplanet survey missions like TESS and PLATO can expect to find multiple examples of truly Earth-like exoplanets with beaches in the 2020s.