The carbon stored globally by plants is shorter-lived and more vulnerable to climate change than previously thought, according to a new study.

The findings have implications for our understanding of the role of nature in mitigating climate change, including the potential for nature-based carbon removal projects such as mass tree-planting.

The research, carried out by an international team led by Dr. Heather Graven at Imperial College London and published in Science, reveals that existing climate models underestimate the amount of carbon dioxide (CO₂) that is taken up by vegetation globally each year, while overestimating how long that carbon remains there.

Dr. Graven, Reader in Climate Physics in Imperial’s Department of Physics, said, “Plants across the world are actually more productive than we thought they were.”

The findings also mean that while carbon is taken up by plants quicker than thought, the carbon is also locked up for a shorter time, meaning carbon from human activities will be released back into the atmosphere sooner than previously predicted.

Dr. Graven added, “Many of the strategies being developed by governments and corporations to address climate change rely on plants and forests to draw down planet-warming CO₂ and lock it away in the ecosystem.

“But our study suggests that carbon stored in living plants does not stay there as long as we thought. It emphasizes that the potential for such nature-based carbon removal projects is limited, and fossil fuel emissions need to be ramped down quickly to minimize the impact of climate change.”

Using carbon

Until now, the rate at which plants use CO₂ to produce new tissues and other parts globally—a measure known as Net Primary Productivity—has been approximated by scaling up data from individual sites. But the sparsity of sites with comprehensive measurements means it has not been possible to accurately calculate Net Primary Productivity globally.

Plants’ productivity has been increasing since the early 1900s and more CO₂ is currently taken up by plants than is released back to the air. Researchers know that approximately 30% of CO₂ emissions by human activities are therefore stored in plants and soils each year, reducing climate change and its impacts.

However, the details of how this storage happens, and its stability into the future, are not yet well understood.

In this study, radiocarbon (¹⁴C)—a radioactive isotope of carbon—was combined with model simulations to understand how plants use CO₂ at a global scale, unlocking valuable insights into the interaction between the atmosphere and the biosphere.

Tracking carbon from bomb tests

Radiocarbon is produced naturally, but nuclear bomb testing in the 1950s and 1960s increased the level of ¹⁴C in the atmosphere. This extra ¹⁴C was available to plants globally, giving researchers a good tool to measure how fast they could take it up.

By examining the accumulation of ¹⁴C in plants between 1963 and 1967—a period when there were no significant nuclear detonations and the total ¹⁴C in the Earth system was relatively constant—the authors could assess how quickly carbon moves from the atmosphere to vegetation and what happens to it once it’s there.

The results show that current, widely-used models that simulate how land and vegetation interact with the atmosphere underestimate the net primary productivity of plants globally. The results also show that the models overestimate the storage time of carbon in plants.

Role of the biosphere

Co-author Dr. Charles Koven, from Lawrence Berkeley National Laboratory, U.S., said, “These observations are from a unique moment in history, just after the peak of atomic weapons testing in the atmosphere in the 1960s.

“The observations show that the growth of plants at the time was faster than current climate models estimate that it was. The significance is that it implies that carbon cycles more rapidly between the atmosphere and biosphere than we have thought, and that we need to better understand and account for this more rapid cycling in climate models.”

The authors say the research demonstrates the need to improve theories about how plants grow and interact with their ecosystems, and to adjust global climate models accordingly, to better understand how the biosphere is mitigating climate change.

Co-author Dr. Will Wieder, from the National Center for Atmospheric Research, U.S., said, “Scientists and policymakers need improved estimates of historical land carbon uptake to inform projections of this critical ecosystem service in future decades. Our study provides critical insights into terrestrial carbon cycle dynamics, which can inform models that are used for climate change projections.”

The work highlights the usefulness of radiocarbon measurements in helping to unpick the complexities of the biosphere. The study’s authors include German physicist Ingeborg Levin, a pioneer in radiocarbon and atmospheric research, who sadly died in February.

An international research team led by the Hong Kong University of Science and Technology (HKUST) has uncovered in a recent research project that people’s beliefs in science and religion are primarily shaped by the words of others, rather than their personal experiences. The study could help enhance public understanding of people’s belief formation in important scientific issues, such as climate change and vaccination.

The team’s findings, realized in collaboration with researchers at Harvard University, Union College, and Massachusetts Institute of Technology, were published in Trends in Cognitive Sciences.

Conventionally, people are generally more confident about the existence of scientific phenomena, like oxygen, than religious phenomena, like God, as it is thought that people can experience oxygen, for instance, while it is harder to observe religious entities on one’s own.

The team, led by Prof. Mary Shaocong MA, a Research Assistant Professor from the Division of Social Science at HKUST, challenged the conventional view. They argued that both scientific and religious beliefs are primarily shaped by testimony or information we get from others, such as experts or our community, rather than personal experience.

The study highlights the decisive role of testimony in forming our beliefs and understanding of the world, contrary to the notion that direct experience is the main driver of scientific belief.

“Even when it seems like we’re experiencing something directly, our understanding is often heavily influenced by what we’ve been told by experts or our community. For example, witnessing a relative falling ill, it’s very hard for a child to detect that viruses cause illness; rather, they turn to others’ testimony, such as parents’ teaching, to understand the causal relations,” says Prof. Ma.

“Recognizing this can help determine the most effective way to communicate scientific information to the public. By highlighting the credibility and consensus of scientific evidence, it is possible to promote greater acceptance and confidence in scientific facts, especially regarding emerging scientific topics, such as climate change.

“This insight is crucial for combating misinformation and enhancing public understanding and support for scientific matters, such as addressing climate change and getting vaccinated,” she further explains.

In the research, by reviewing empirical evidence in the past few decades, the team proposed a theoretical model that explains how people come to believe in the existence of invisible entities, such as germs in science or God in religion.

For example, it finds that people believe in germs because doctors and scientists tell us they exist, even though we cannot see them with our own eyes. Likewise, we infer that people get sick because of germs by learning this causal relation from others rather than discovering this connection through personal observation.

The model also establishes that the more credible the source of the information and the more people who agree with it, the more likely people are to believe it. “If many people around us agree that climate change is real, their consensus strengthens our belief in these concepts,” she says.

It shows that people’s confidence in these phenomena is not because they have seen them directly but because they trust the sources that tell them about them.

Unlike previous models that proposed separate pathways for belief formation in science and religion, this model provides a unified explanation. It argues that others’ testimony, rather than direct experience, predominantly shapes beliefs in both domains.

Data assimilation is a mathematical discipline that integrates observed data and numerical models to improve the interpretation and prediction of dynamical systems. It is a crucial component of Earth sciences, particularly in numerical weather prediction (NWP).

Data assimilation techniques have been widely investigated in NWP in the last two decades to refine the initial conditions of weather models by combining model forecasts and observational data. Most NWP centers around the world employ variational and ensemble-variational data assimilation methods, which iteratively reduce cost functions via gradient-based optimization. However, these methods require significant computational resources.

Recently, quantum computing has emerged as a new avenue of computational technology, offering a promising solution for overcoming the computational challenges of classical computers.

Quantum computers can take advantage of quantum effects such as tunneling, superposition, and entanglement to significantly reduce computational demands. Quantum annealing machines, in particular, are powerful for solving optimization problems.

In a study, published in Nonlinear Processes in Geophysics, Professor Shunji Kotsuki from the Institute for Advanced Academic Research/Center for Environmental Remote Sensing/Research Institute of Disaster Medicine, Chiba University, along with his colleagues Fumitoshi Kawasaki from the Graduate School of Science and Engineering and Masanao Ohashi from the Center for Environmental Remote Sensing, developed a novel data assimilation technique designed for quantum annealing machines.

“Our study introduces a novel quantum annealing approach to accelerate data assimilation, which is the main computational bottleneck for numerical weather predictions. With this algorithm, we successfully solved data assimilation on quantum annealers for the first time,” explains Prof. Kotsuki.

In the study, the researchers focused on the four-dimensional variational data assimilation (4DVAR) method, one of the most widely used data assimilation methods in NWP systems. However, since 4DVAR is designed for classical computers, it cannot be directly used on quantum hardware.

Prof. Kotsuki says, “Unlike the conventional 4DVAR, which requires a cost function and its gradient, quantum annealers require only the cost function. However, the cost function must be represented by binary variables (0 or 1). Therefore, we reformulated the 4DVAR cost function, a quadratic unconstrained optimization (QUO) problem, into a quadratic unconstrained binary optimization (QUBO) problem, which quantum annealers can solve.”

The researchers applied this QUBO approach to a series of 4DVAR experiments using a 40-variable Lorentz-96 model, which is a dynamical system commonly used to test data assimilation.

They conducted the experiments using the D-Wave Advantage physical quantum annealer, or Phy-QA, and the Fixstars Amplify’s simulated quantum annealer, or Sim-QA. Moreover, they tested the conventionally utilized quasi-Newton-based iterative approaches, using the Broyden-Fletcher-Goldfarb-Shanno formula, in solving linear and nonlinear QUO problems and compared their performance to that of quantum annealers.

The results revealed that quantum annealers produced analysis with comparable accuracy to conventional quasi-Newton-based approaches but in a fraction of the time they took.

The D-Wave’s Phy-QA required less than 0.05 seconds for computation, much faster than conventional approaches. However, it also exhibited slightly larger root mean square errors, which the researchers attributed to the inherent stochastic quantum effects.

To address this, they found that reading out multiple solutions from the quantum annealer improved stability and accuracy. They also noted that the scaling factor for quantum data assimilation, which is important for regulating the analysis accuracy, was different for the D-Wave Phy-QA and the Sim-QA, owing to the stochastic quantum effects associated with the former annealer.

These findings signify the role of quantum computers in reducing the computational cost of data assimilation.

“Our approach could revolutionize future NWP systems, enabling a deeper understanding and improved predictions with much less computational time. In addition, it has the potential to advance the practical applications of quantum annealers in solving complex optimization problems in Earth science,” says Prof. Kotsuki.

Overall, the proposed innovative method holds great promise for inspiring future applications of quantum computers in advancing data assimilation, potentially leading to more accurate weather predictions.