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  • Location, Location, Location: The “Where” of Reforestation May Matter More Than the Extent Faith Ishii
    Planting more trees will decelerate climate change only if those trees are placed in optimal locations—primarily the tropics and subtropics—suggests new research published in Communications Earth and Environment. However, planting trees in locations like Alaska, Siberia, and large parts of the United States could actually lead to warming, said lead author and doctoral student at ETH Zurich Nora Fahrenbach. Much of the current thinking in nature-based solutions, Fahrenbach said, is ba
     

Location, Location, Location: The “Where” of Reforestation May Matter More Than the Extent

22 April 2026 at 12:36
A forest at golden hour

Planting more trees will decelerate climate change only if those trees are placed in optimal locations—primarily the tropics and subtropics—suggests new research published in Communications Earth and Environment. However, planting trees in locations like Alaska, Siberia, and large parts of the United States could actually lead to warming, said lead author and doctoral student at ETH Zurich Nora Fahrenbach.

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Much of the current thinking in nature-based solutions, Fahrenbach said, is based on the idea that “more is better.”

As in, “we’ll plant a trillion trees, or we’ll plant more than a trillion trees, and we are going to get more cooling, right?” Fahrenbach said. “That’s something we show is just not the case.”

Fahrenbach researches reforestation potentials, or global maps that identify areas where trees could be planted to mitigate climate change. In this work, she and her colleagues compared three prominent reforestation potentials to determine the effect of tree placement on local and global temperatures.

One scenario involved reforesting about 926 million hectares focused mostly on the tropics and resulted in about 0.25°C of cooling by 2100. Another called for reforesting 894 million hectares, including large areas in northern temperate and polar latitudes, and resulted in 0.13°C of cooling by 2100.

The third scenario involved planting forests strategically over only 440 million hectares of mostly tropical and subtropical land (less than half of the area covered in the other scenarios) but also resulted in 0.13°C of cooling. Geography, the findings suggest, may matter more than quantity when it comes to the cooling benefits of reforestation efforts.

Let’s Get (Biogeo)physical

The researchers modeled all three scenarios using the same parameters: Trees were planted from 2015 to 2070 and then remained steady in their population until 2100.

Planting trees in one area doesn’t just change the local temperature but has effects across the world.

All three models identified reforestation opportunities in regions such as the eastern United States, Amazonia, the Congo rainforest, and eastern China, as well as regions for which reforestation would not be as impactful, such as polar regions in the Northern Hemisphere. The researchers also found significant temperature changes across the Atlantic and Indian oceans as a result of atmospheric changes induced by reforestation, demonstrating an interconnected reality: Planting trees in one area doesn’t just change the local temperature but has effects across the world.

These local and nonlocal effects can be explained by a combination of biogeochemical and biogeophysical effects.

A biogeochemical effect relates to the movement of chemicals or chemical elements, such as trees absorbing carbon from the atmosphere.

A biogeophysical effect relates to the physical results of changing the land’s surface: Placing a tree in a snowy region, for instance, decreases the land’s albedo, meaning it causes the land surface to become darker and absorb more light, leading to more local heat. This rise in surface temperature also raises air temperature, creating cascading effects on wind patterns and oceanic currents.

Considering both processes together is essential for understanding whether a net cooling or net heating effect exists, but most policies focus only on biogeochemical effects, seeing trees solely for their ability to absorb carbon from the atmosphere, Fahrenbach said. They include prominent international policies such as the Paris Agreement and the United Nations’ Framework for REDD+.

“Really, we would also need to consider the biogeophysical effects,” Fahrenbach said. “That’s harder to do, right, considering those nonlocal effects, because just imagine, some country is going to plant a lot of trees, and that’s going to lead to warming somewhere else.”

A Call to Policymakers

Emilio Vilanova, a forest ecologist at the climate action nonprofit Verra, wrote by email, “The most important message for me is that this study emphasizes something that is often not well addressed in reforestation projects: Reforestation is not just about planting trees—it’s about designing where new forests go to maximize benefits and avoid unintended consequences.”

“Reforestation is a helpful tool, not a stand-alone solution to climate change.”

Vilanova also said the study puts the potential for reforestation efforts to address climate change in perspective. “Even very large reforestation efforts would only reduce global temperatures by about 0.13–0.25°C by the end of the century,” he said. “While meaningful, this finding also reinforces that reforestation is a helpful tool, not a stand-alone solution to climate change.”

Though the limited potential for change is sobering, the authors and Vilanova pointed out that this change does matter and that it matters how we think of our approach. They advocate for policies that adopt reforestation strategies based on location and that acknowledge both the local and nonlocal effects of reforestation.

“We really need to make sure that where we plant first, it has benefits locally, it has benefits globally,” Fahrenbach said.

—Andrew Meissen (@AndrewMeissen), Science Writer

22 April 2026: This article was updated to correct Nora Fahrenbach’s position at ETH Zurich.

This news article is included in our ENGAGE resource for educators seeking science news for their classroom lessons. Browse all ENGAGE articles, and share with your fellow educators how you integrated the article into an activity in the comments section below.

Citation: Meissen, A. (2026), Location, location, location: The “where” of reforestation may matter more than the extent, Eos, 107, https://doi.org/10.1029/2026EO260125. Published on 22 April 2026.
Text © 2026. The authors. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.
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  • Machine Learning Can Improve the Use of Atmospheric Observations in the Tropics  Istvan Szunyogh
    Editors’ Highlights are summaries of recent papers by AGU’s journal editors. Source: Journal of Advances in Modeling Earth Systems The purpose of atmospheric data assimilation is to obtain a 3-dimensional gridded representation of the fields of the atmospheric state variables (temperature, wind, pressure, etc.) for a specific time based on atmospheric observations. The product of data assimilation, called analysis, can be used to prepare weather maps and to start model-based weather forec
     

Machine Learning Can Improve the Use of Atmospheric Observations in the Tropics 

14 April 2026 at 12:00
Illustration from the article.
Editors’ Highlights are summaries of recent papers by AGU’s journal editors.
Source: Journal of Advances in Modeling Earth Systems

The purpose of atmospheric data assimilation is to obtain a 3-dimensional gridded representation of the fields of the atmospheric state variables (temperature, wind, pressure, etc.) for a specific time based on atmospheric observations. The product of data assimilation, called analysis, can be used to prepare weather maps and to start model-based weather forecasts. Analyses collected over a long period of time can also be used for research and to monitor variability and changes in the climate.

The main challenges of data assimilation are that observations are not collocated with the grid-points of the analysis, and most observations do not observe the variables of interest directly and have errors. For example, satellite-based observations, which form the bulk of the operationally assimilated observations, measure the intensity of electro-magnetic waves at the top of the atmosphere; a physical quantity that depends on the atmospheric state in highly complicated ways. The background-error covariance matrix is a key component of a data assimilation system, responsible for spreading information from observations to the unobserved locations and state variables. A good estimate of this matrix is essential to produce analyses in which the fields of the state variables are realistic and consistent with each other. Obtaining such an estimate is particularly challenging for tropical locations, where physics-based knowledge does not lead to a straightforward practical formulation.

In a new study, Melinc et al. [2026] propose a novel machine learning-based (ML-based) approach to define a background-error matrix that is equally effective in the midlatitudes and tropics. This approach takes advantage of the power of ML to learn quantitative relationships between different state variables at different locations-relationships that are either not known, or cannot be easily used for the formulation of a background-error matrix based on physics-based knowledge.

Citation: Melinc, B., Perkan, U., & Zaplotnik, Ž. (2026). A unified neural background-error covariance model for midlatitude and tropical atmospheric data assimilation. Journal of Advances in Modeling Earth Systems, 18, e2025MS005360. https://doi.org/10.1029/2025MS005360

—Istvan Szunyogh, Associate Editor, JAMES

Text © 2026. The authors. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.
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