The importance of ENSO phase during volcanic eruptions for detection and attribution. Issue 6 (29th March 2016)

• Record Type: Journal Article
• Authors: Lehner, Flavio; Schurer, Andrew P.; Hegerl, Gabriele C.; Deser, Clara; Frölicher, Thomas L.
• Is Part Of: Geophysical research letters. Volume 43:Issue 6(2016)
• Page Start: 2851
• Page End: 2858
• Publication Date: 2016-03-29
• Subjects: volcanic response -- detection and attribution -- ENSO phase -- model climate sensitivity -- climate variability; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1002/2016GL067935
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: Comparisons of the observed global‐scale cooling following recent volcanic eruptions to that simulated by climate models from the Coupled Model Intercomparison Project 5 (CMIP5) indicate that the models overestimate the magnitude of the global temperature response to volcanic eruptions. Here we show that this overestimation can be explained as a sampling issue, arising because all large eruptions since 1951 coincided with El Niño events, which cause global‐scale warming that partially counteracts the volcanically induced cooling. By subsampling the CMIP5 models according to the observed El Niño–Southern Oscillation (ENSO) phase during each eruption, we find that the simulated global temperature response to volcanic forcing is consistent with observations. Volcanic eruptions pose a particular challenge for the detection and attribution methodology, as their surface impacts are short‐lived and hence can be confounded by ENSO. Our results imply that detection and attribution studies must carefully consider sampling biases due to internal climate variability. Key Points: There is disagreement between observed and simulated global mean cooling response to recent volcanic eruptions Accounting for the observed coincidence of El Niño events with recent eruptions resolves this bias Detection and attribution studies must consider sampling biases arising from internal variability