Smarter climate models: How plants influence the water cycle

Earth system models (ESMs) are essential tools for studying complex planetary processes, including interactions between the atmosphere and biosphere. These models help scientists and policymakers better understand climate change and its impacts. 

However, improving the accuracy of Earth system models often requires gathering vast amounts of data – an effort that can take decades, if not centuries.

Expanding plant data for climate models 

A new study published in the journal Nature Scientific Data presents a breakthrough approach to this challenge. The researchers have developed a method to bypass the need for extensive field data collection across all known tree species. 

Instead, they leveraged evolutionary relationships between plants to estimate key hydrologic traits, significantly expanding the data available for global climate modeling.

Study lead author James Knighton is an assistant professor in the University of Connecticut’s Department of Natural Resources and the Environment.

Pablo Sanchez-Martinez from the University of Edinburgh and Leander Anderegg from the University of California Santa Barbara are co-authors of the study. 

The role of plants in water movement

Plants play a fundamental role in Earth’s environmental systems, from capturing carbon and producing oxygen to influencing the movement of water across ecosystems. 

According to Knighton, an estimated 60% of all rainwater is returned to the atmosphere through plant transpiration, a process that moves water from soil to air through plant tissues.

This water movement is currently represented in ESMs in a simplified manner, often grouping plants into broad categories called Plant Functional Types (PFTs).

“Plant Functional Types (PFTs) are used because we don’t know a lot about the details of individual plant species,” Knighton explained. “It would be harder to take a detailed map of vegetation over a continent and put in all the right values for each individual species, so it’s easier just to consider one generic PFT.”

While PFTs provide a manageable approach for modeling, they overlook key differences in plant hydrologic traits – such as root depth, water transport efficiency, and transpiration rates – that can significantly affect climate projections.

A new approach to climate models

To improve the representation of plant traits in climate models, scientists have built databases like the TRY Plant Trait Database, which compiles field measurements of plant characteristics. However, progress in cataloging traits has been slow.

“There are around 60,000 to 70,000 tree species on Earth, meaning that after 200 years, we know maybe 5 to 10% of what’s happening,” noted Knighton. 

“If that were the way we would do things, it would take us another 2,000 years or so to learn about all the plants that we needed to, and at that point, climate change has set in, and it’s too late.”

Given the urgency of climate modeling, waiting for field researchers to collect direct measurements is not a viable solution. Instead, Knighton and his colleagues devised a faster method to estimate missing plant traits using evolutionary relationships.

Using evolution to predict hydrologic traits

To address the knowledge gap, the researchers turned to phylogenetics, the study of evolutionary relationships between species. They examined available trait data – such as tree height, root depth, and water transport speed – across known species and then analyzed how closely related species share these traits.

“We looked to see how similar trait values are between closely related species, and the idea behind that is, if these traits are critical for their survival, evolution will have preserved the trait values, they won’t be randomly dispersed,” Knighton explained. 

“For example, if growing deep roots was critical for a certain type of plant to survive, the species that branch off from that one will probably also have deep roots, and everything that’s in that family or that genus will have a similar root structure.”

Missing data for thousands of tree species 

The researchers found that hydrologic traits are highly conserved across evolutionary lineages, meaning that closely related species tend to have similar characteristics. This discovery enabled them to predict missing data for over 55,000 tree species by inferring trait values from their known relatives.

“We used different numerical machine learning techniques, and in doing that, we were able to come up with a database of these very critical tree values for 55,000 tree species on Earth,” Knighton said. 

This approach allows modelers to move beyond the oversimplified PFT system, providing a much more detailed representation of plant diversity in climate simulations.

Confirming the estimated plant traits

While the research marks a major step forward, the scientists acknowledge that their method represents a first approximation. The next phase of their work involves validating the estimated traits against real-world observations.

“We consider this work to be a low-order approximation, but it is an important starting point,” Knighton explained. “As more data is collected from field researchers, the data can be used to update and refine the interpolated data to improve the accuracy of this approach.”

To test their estimates, the researchers are comparing their predictions against observational data from 10 well-documented forest sites across the United States.

Master’s student Caroline Stanton is currently constructing ecosystem models for each site and calibrating them with high-resolution plant trait data. 

These models will then be compared to field data collected over the past two decades to assess how well the inferred traits match real-world measurements.

A new tool for climate simulation

If successful, the method could be applied to forested ecosystems worldwide. Beyond simply improving climate models, the dataset could help scientists understand what drives variation in plant traits and how these variations affect global environmental processes.

“If you want to do global modeling that includes more detail in the vegetation, which is important, you now have a starting point,” Knighton emphasized.

By dramatically expanding the dataset available for climate simulations, this research provides a new tool for modelers, ecologists, and policymakers working to predict and mitigate the impacts of climate change. 

With further refinement, it could play a key role in shaping more accurate forecasts of Earth’s future climate and water cycles.

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