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原文連結
論文資訊
- 類型：已發表論文
- 日期：2021-03-29
摘要
In September of 2020, Arctic sea ice extent was the second-lowest on record. State of the art 氣候 prediction uses Earth system models (ESMs), driven by systems of differential equations representing the laws of physics. Previously, these models have tended to underestimate Arctic sea ice loss. The issue is grave because accurate modeling is critical for 經濟, ecological, and geopolitical planning. We use 機器學習 techniques, including random 森林 regression and Gini importance, to show that the Energy Exascale Earth System Model (E3SM) relies too heavily on just one of the ten chosen climatological quantities to predict September sea ice averages. Furthermore, E3SM gives too much importance to six of those quantities when compared to observed data. Identifying the features that 氣候 models incorrectl
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