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Publication Date
28 June 2020

How Have Local Radiative Feedbacks Contributed to Polar Amplification Since 1980?

Subtitle
New simulations focused on recent temperature change add confidence to modeling many radiative feedbacks, but estimating the role of clouds remains a challenge.
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The Arctic region is one of the three regions on Earth where the surface temperature has risen more strongly than the global average. This phenomenon is commonly known as polar amplification.
Science

The global mean temperature at the Earth's surface has risen rapidly since 1980, and the warming isn’t uniform. Three of the planet’s regions—the Arctic, Antarctic, and Tibetan Plateau (sometimes called the “three poles”)—have warmed more than the global average, a phenomenon commonly known as polar amplification. Feedbacks are mechanisms that intensify or reduce interactions between Earth system processes in the presence of an outside influence, and radiative feedbacks involving temperature, snow and ice, and clouds are believed to contribute to polar amplification. Scientists at the U.S. Department of Energy’s Pacific Northwest National Laboratory led a study to quantify these feedbacks using historical short-term climate simulations. These simulations can reproduce observed warming and polar amplification.

Impact

This research is the first systematic quantification of individual radiative feedbacks over the “three poles” based on historical short-term (1980–2017) simulations from multiple state-of-the-art climate models. The global mean net feedback in the simulations is estimated to be negative; this means that the sum of all identified feedbacks decreases the historical warming. The magnitude of the net negative feedback identified using the recent climate record appears stronger than that estimated by previous studies that used longer simulations and examined polar amplification in the presence of quadrupled carbon dioxide levels. The previous work estimated more rapid warming. The differences in estimates of the net negative feedback are primarily explained by the global‐mean cloud feedback being quite small in recent decades, though it is estimated to become larger in the future. All models agree that the temperature lapse rate feedback is the largest contributor to polar amplification.

Summary

Previously, the team showed that incomplete knowledge of the evolving effective radiative forcing from changing greenhouse gases, aerosols, and land conditions leads to uncertainty in quantifying the feedbacks in historical short‐term climate simulations. This study extends that work by analyzing a new unique set of atmospheric general circulation model (AGCM) simulations. The new simulations are drawn from the Atmospheric Model Intercomparison Project within CMIP phase 6 (AMIP6) with known effective radiative forcing for 1980–2014 and a specifically designed Community Atmosphere Model version 5 (CAM5) simulation with constant effective radiative forcing for 1980–2017.

The historical global mean net feedback estimated from the AGCM simulations is around −2 W m-2 K-1, about twice the magnitude estimated from dozens of longer‐term warming experiments driven by quadrupled levels of atmospheric carbon dioxide. This difference is mainly caused by near‐zero net cloud feedback for the historical time period in short-term simulations. The team also showed that the temperature lapse rate feedback for 1980–2014/2017 is the largest contributor to the amplified temperature change over the three poles, followed by surface albedo feedback and Planck feedback deviation from its global mean. Interestingly, except for a higher surface albedo feedback in the Antarctic region, all other feedbacks are similar between the Arctic and Antarctic. The largest disagreement between the CAM5 and the AMIP6 model results is in both shortwave and longwave cloud feedbacks that differ in sign as well as magnitude. This result calls for further investigation of why this uncertainty in global and regional cloud feedback exists in climate models. 

Point of Contact
Hailong Wang
Institution(s)
Pacific Northwest National Laboratory (PNNL)
Funding Program Area(s)
Publication