A model to tell earth’s natural climatic patterns apart from human-caused climate change

What is it about?

Earth’s climate is a complex and dynamic system—air temperature varies regionally and globally over time not only due to human-caused global warming, but also because of natural climatic phenomena. Telling apart human-caused (forced) climate change from Earth’s natural (internal) climate patterns is an active area of research. In this study, the authors developed an objective technique that mathematically decomposes the complex behavior of Earth’s surface air temperature into simpler components called ‘nodes’. Their model, which was fed with temperature data gathered globally throughout the 20th century, proved capable of discerning many of Earth’s internal nodes, such as the well-known El Niño Southern Oscillation, from the forced node, yielding insight into the dynamic interplay between them.

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Photo by Johannes Plenio on Unsplash

Photo by Johannes Plenio on Unsplash

Why is it important?

Understanding the true nature of forced climate change is much more challenging than simply calculating changes in average global temperature. The effects of forced global warming vary regionally and can over-lap and alter natural climatic patterns. Thus, building models capable of explaining climate change through the vast amounts of observed climate data is essential. The researchers used their model to decompose the complex system that is Earth’s surface temperature over different regional and time scales. Surprisingly, they found that the forced node corresponding to human-caused global warming exhibits patterns similar in time and space to two newly discovered internal nodes. In a sense, this could indicate that Earth’s natural climatic patterns dynamically affect the spatial distribution of forced climate change. KEY TAKEAWAY: Climate scientists should strive to better understand the complex interplay between human-caused climate change and the newly discovered natural climatic patterns, which flew under the radar in previous studies.