Wednesday, July 30, 2025

 

Research improves accuracy of climate models – particularly for extreme events




North Carolina State University






Researchers have devised a new machine learning method to improve large-scale climate model projections and demonstrated that the new tool makes the models more accurate at both the global and regional level. This advance should provide policymakers with improved climate projections that can be used to inform policy and planning decisions.

“Global climate models are essential for policy planning, but these models often struggle with ‘compound extreme events,’ which is when extreme events happen in short succession – such as when extreme rainfall is followed immediately by a period of extreme heat,” says Shiqi Fang, first author of a paper on the work and a research associate at North Carolina State University.

“Specifically, these models struggle to accurately capture observed patterns regarding compound events in the data used to train the models,” Fang says. “This leads to two additional problems: difficulty in providing accurate projections of compound events on a global scale; and difficulty in providing accurate projections of compound events on a local scale. The work we’ve done here addresses all three of those challenges.”

“All models are imperfect,” says Sankar Arumugam, corresponding author of the paper and a professor of civil, construction and environmental engineering at NC State. “Sometimes a model may underestimate rainfall, and/or overestimate temperature, or whatever. Model developers have a suite of tools that they can use to correct these so-called biases, improving a model’s accuracy.

“However, the existing suite of tools has a key limitation: they are very good at correcting a flaw in a single parameter (like rainfall), but not very good at correcting flaws in multiple parameters (like rainfall and temperature),” Arumugam says. “This is important, because compound events can pose serious threats and – by definition – involve societal impacts from two physical variables, temperature and humidity. This is where our new method comes in.”

The new method takes a novel approach to the problem and makes use of machine learning techniques to modify a climate model’s outputs in a way that moves the model’s projections closer to the patterns that can be observed in real-world data.

The researchers tested the new method – called Complete Density Correction using Normalizing Flows (CDC-NF) – with the five most widely used global climate models. The testing was done at both the global scale and at the national scale for the continental United States.

“The accuracy of all five models improved when used in conjunction with the CDC-NF method,” says Fang. “And these improvements were especially pronounced with regard to accuracy regarding both isolated extreme events and compound extreme events.”

“We have made the code and data we used publicly available, so that other researchers can use our method in conjunction with their modeling efforts – or further revise the method to meet their needs,” says Arumugam. “We’re optimistic that this can improve the accuracy of projections used to inform climate adaptation strategies.”

The paper, “A Complete Density Correction using Normalizing Flows (CDC-NF) for CMIP6 GCMs,” is published open access in the Nature journal Scientific Data. The paper was co-authored by Emily Hector, an assistant professor of statistics at NC State; Brian Reich, the Gertrude M. Cox Distinguished Professor of Statistics at NC State; and Reetam Majumder, an assistant professor of statistics at the University of Arkansas.

The work was made possible by the National Science Foundation, under grants 2151651 and 2152887.

How climate shapes soil fungal traits


Study reports on global distributions of microbial traits with applications for soil health



Dartmouth College

Map showing study sites and biomes. 

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Global distribution of 3,500+ study sites used in the analysis of AM fungal spore traits across diverse biomes. 

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Credit: Map by Smriti Pehim Limbu.




Many soil microbes play a vital role in ecosystems, as they help plants access nutrients and water and assist in stress tolerance such as during drought and to defend against pathogens.

One such group of soil microbes are arbuscular mycorrhizal, aka AM, fungi. These important fungi are essential to plant health and are associated with the roots of approximately 70% of plant species on land. Through their symbiotic relationship with plant roots, the fungi contribute significantly to the carbon cycle and other processes that sustain ecosystem functioning.

A fungus's spores are responsible for fungal reproduction and dispersal. And spore traits, including volume, cell wall thickness, ornamentation such as projections or depressions in the cell wall, shape, and color, can affect how well fungi survive in different environments.

A new Dartmouth-led study reports on how global climate conditions affect AM fungal spore traits and the species biogeographic patterns. The study is the first to examine multiple traits of this kind on a global scale. The results are published in the Proceedings of the National Academy of Sciences

"As climate change continues, we expect shifts in these microbial traits that influence how these fungi survive, spread, and interact with plants, which could have cascading effects across ecosystems, and affect restoration efforts and food production," says lead author Smriti Pehim Limbu, a postdoctoral fellow in the Ecology, Evolution, Environment & Society Program and member of the Chaudhary Ecology Lab at Dartmouth.

For the study, the researchers synthesized data from different global databases of AM fungal species with climate data, to examine how climate affects the spore traits. These included TraitAM—a public database of the spore traits of more than 340 AM fungi, created by senior author Bala Chaudhary, an associate professor of environmental studies at Dartmouth.

"Our findings showed that spores that were bigger and darker in color were more common in warm, wet climates, but there was a trade-off between persistence and dispersal," says Pehim Limbu. "While being bigger helped the spores to persist in warm, wet conditions, these conditions were associated with a more limited geographical distribution."

Spores with more cell surface ornamentation were also more common in warm, wet climates but had smaller geographic distributions. Darker spores, which have more pigment, were more common in warm, wet climates. According to the co-authors, those attributes may help protect the fungi from ultraviolet radiation and fire. 

Yet, cell wall thickness for spores decreased in warm, wet climates and was more robust in cooler, drier climates. Intermediate cell wall thickness was found to be associated with broader geographic distribution.

Global distribution of 3,500+ study sites used in the analysis of AM fungal spore traits across diverse biomes. Map by Smriti Pehim Limbu.

By understanding which AM fungal spore traits thrive in specific climates such as dry versus humid climatic conditions, the co-authors report that the findings could guide commercial applications of bioinoculants, microbial amendments used for soil restoration, through selection of AM fungi suited to the local environment.

"Ecologists since before Darwin have been studying the geographic distribution of species’ traits," says Chaudhary. "For example, we know that mammals with white fur are more likely to occur in cold climates. This study takes an important step in uncovering similar patterns for the traits of microbes, giving insight into the environmental adaptations of the majority of biodiversity on Earth," says Chaudhary.

Study co-authors Pehim Limbu (Smriti.Pehim.Limbu@dartmouth.edu) and Chaudhary (Bala.Chaudhary@dartmouth.edu) are available for comment.

Sidney Stürmer at the Universidade Regional de Blumenau in Brazil, Geoffrey Zahn at William & Mary, Carlos Aguilar-Trigueros at University of Jyväskylä in Finland, and Noah Rogers at Utah Valley University, also contributed to the research.

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