Work

Internal Climate Variability Obscures Future Freezing Rain Changes Despite Global Warming Trend

Year: 2024

Type: article

Abstract: Although numerous studies have projected changes in freezing rain under future climate conditions, the internal variability of freezing rain remains poorly quantified. Here, we introduce a framework utilizing a novel machine‐learning algorithm to diagnose freezing rain in reanalysis and climate model simulations. By employing multivariate quantile mapping, we decompose the projected freezing rain trend into contributions from changes in temperature, relative humidity, and precipitation, which helps separate the forced response from internal climate variability. Our finding reveals a notable decrease in freezing rain occurrence in most areas. Despite a substantial temperature increase, internal variability overshadows climate forcing across a large portion of the eastern United States until about 2050. This insight has implications for practitioners, suggesting that the observed freezing rain frequency climatology continues to provide a relevant baseline for decision‐making in the near term. However, longer‐term design and adaptation plans should consider the projected changes in these regions.

Source: Geophysical Research Letters

DOI: 10.1029/2024gl111741

Authors: Haoyu (Richard) Zhuang, Arthur T. DeGaetano, Flavio Lehner

Institutions: University of Washington, Cornell University, NSF National Center for Atmospheric Research, NSF NCAR Climate and Global Dynamics Laboratory

Language: English

Topic: Climate variability and models

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,900