Amazon dieback beyond the 21st century under high-emission scenarios by Earth System models

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Conflicts of Interest

Identified Weaknesses

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Paper Summary

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Amazon Rainforest Could Face Severe Decline by 2300, Climate Models Predict

Climate models project that a large portion of the Amazon rainforest could experience severe decline by the end of the 23rd century under a high greenhouse gas emission scenario. Key factors contributing to this decline are rising temperatures, decreasing rainfall, and land-use changes, specifically the conversion of forest to pasture or cropland. The decline is projected to be gradual, spanning over a century, eventually leading to a temporary shift from a carbon sink to a carbon source in the 22nd century before returning to near-neutral conditions by the 23rd century.

Possible Conflicts of Interest

None identified

Identified Weaknesses

Model limitations

The paper's findings rely on the accuracy and limitations of these models, which may not perfectly represent the complexity of the Amazonian ecosystem. The coarse spatial resolution of many ESMs limits the ability to accurately capture the transition between different ecosystems and local feedbacks.

Limited fire representation

Fire impacts are underestimated, as models include a limited representation of fire processes in the Amazon Basin.

Focus on long-term trends

The paper's focus is on long-term trends and the cumulative impacts of gradual and sustained changes over centuries. Short-term fluctuations and more rapid events might be underestimated.

Limited exploration of deforestation impact

The authors suggest a safe boundary of 10% deforestation, but acknowledge more analysis is needed to understand the degree to which land-use change worsens dieback risk.

Rating Explanation

This research uses state-of-the-art climate models to make important predictions about the future of the Amazon rainforest, which is crucial for the global climate and biodiversity. The study acknowledges the inherent limitations of these models and identifies areas for improvement. The methodology is strong, and the findings are significant.

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