The Complexity of Amplified Arctic Warming can be Understood Analytically

The Science    

The Arctic is warming much faster than the rest of the Earth, a phenomenon known as Arctic amplification (AA). AA is evident in historical observations, data from ancient climates, and computer model simulations. AA has significant effects on the Arctic's ice and ecosystems and also influences global weather patterns and climate, hence the importance of better understanding its magnitude and behavior. However, the degree of AA (commonly defined as the ratio in surface temperature changes between the Arctic and global mean) varies widely by a factor of two among climate models. This variation presents a challenge. While substantial achievements have been made regarding the physics of AA, understanding how the degree of AA is determined remains limited as several outstanding questions remain: What are the essential physics that determine the degree of AA? What is the functional relationship between the degree of AA and the key physical parameters? Why does the degree of AA vary among climate models from 1.5 to 3.5 but not go beyond? What causes the variation in the degree of AA among models?

The Impact

The results of this study reveal that the degree of AA, despite being controlled by complicated interactions among multiple factors, can be analytically understood. Researchers derived that the degree of AA is a nonlinear function of five key physical factors. The results of this formula help to address four key questions about AA.

First, the formula accurately explains the differences in AA seen in various climate models and attributes the variation to specific physical factors that can be targeted by the modelling centers.

Second, it shows how different physical processes work together to set the degree of AA, how AA is confined within a range of 1.5 to 3.5, and how AA will change if certain conditions change.

Third, the formula articulates the crucial role of atmospheric heat transport (AHT) in AA. The existence of AHT creates a baseline AA, which exists even when other factors are equal between the global and the Arctic. This study shows that the effect of AHT should not be interpreted from its total change but its partial sensitivities to warming.

Lastly, the results clarify that while the lapse-rate feedback is a major factor in AA, its effect is balanced out by the water-vapor feedback. The theory put forth in this study works beyond current diagnostic understanding of AA. It can help improve climate models and predict future changes in the Arctic and global climate more accurately.

Summary

AA is a phenomenon where the Arctic warms faster than the rest of the globe, a key feature of climate change. While the basic concept and the contributing factors of AA are known, how the degree of AA is determined is not well understood. In this study, by developing a two-box energy-balance model that considers all essential physics of AA and particularly the forcing and feedback mechanism of AHT, researchers derived that the degree of AA is determined by a simple, nonlinear function of five key physical parameters. These include Arctic and global feedback, variations in radiative forcing across latitudes, and the sensitivity of AHT to overall global warming and temperature differences between the Arctic and other regions. This analytic theory explains why different climate models show varying degrees of AA and why the degree of AA ranges from 1.5 to 3.5 rather than being much higher.

The theory works beyond current diagnostic methods, providing a clearer understanding of how specific physical factors control AA. It offers predictive insights into how changes in these factors can alter AA. Additionally, this model can be used to study Antarctic warming and may help predict future changes in polar amplification.

PNNL Contact

L. Ruby Leung, Pacific Northwest National Laboratory, ruby.leung@pnnl.gov

Funding

This research was supported by the Department of Energy, Office of Science, as part of research in the Regional and Global Model Analysis program area, Earth and Environmental System Modeling Program.