Abstract

Abstract. The global temperature responses to the eruptions of Mount Agung in 1963, El Chichón in 1982, and Mount Pinatubo in 1991 are investigated using nine currently available reanalysis data sets (JRA-55, MERRA, ERA-Interim, NCEP-CFSR, JRA-25, ERA-40, NCEP-1, NCEP-2, and 20CR). Multiple linear regression is applied to the zonal and monthly mean time series of temperature for two periods, 1979–2009 (for eight reanalysis data sets) and 1958–2001 (for four reanalysis data sets), by considering explanatory factors of seasonal harmonics, linear trends, Quasi-Biennial Oscillation, solar cycle, and El Niño Southern Oscillation. The residuals are used to define the volcanic signals for the three eruptions separately, and common and different responses among the older and newer reanalysis data sets are highlighted for each eruption. In response to the Mount Pinatubo eruption, most reanalysis data sets show strong warming signals (up to 2–3 K for 1-year average) in the tropical lower stratosphere and weak cooling signals (down to −1 K) in the subtropical upper troposphere. For the El Chichón eruption, warming signals in the tropical lower stratosphere are somewhat smaller than those for the Mount Pinatubo eruption. The response to the Mount Agung eruption is asymmetric about the equator with strong warming in the Southern Hemisphere midlatitude upper troposphere to lower stratosphere. Comparison of the results from several different reanalysis data sets confirms the atmospheric temperature response to these major eruptions qualitatively, but also shows quantitative differences even among the most recent reanalysis data sets. The consistencies and differences among different reanalysis data sets provide a measure of the confidence and uncertainty in our current understanding of the volcanic response. The results of this intercomparison study may be useful for validation of climate model responses to volcanic forcing and for assessing proposed geoengineering by stratospheric aerosol injection, as well as to link studies using only a single reanalysis data set to other studies using a different reanalysis data set.

Highlights

  • Explosive volcanic eruptions inject sulphur species to the stratosphere in the form of SO2 and H2S which convert to H2SO4 aerosols

  • Comparing with the results from Mitchell et al (2015) who used a regression analysis with different details, the setting of this effective degree of freedom may be somewhat too conservative. This is because the regions evaluated as statistically significant are smaller than those in Mitchell et al (2015) for the solar and El Niño Southern Oscillation (ENSO) signals in the tropical lower stratosphere, but the general features are quite similar to those shown in Mitchell et al (2015) they considered a volcanic index www.atmos-chem-phys.net/15/13507/2015/

  • The multiple regression analysis is applied to the four reanalysis data sets, namely, JRA-55, ERA-40, NCEP-1, and 20CR which cover the period of 1958–2001

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Summary

Introduction

Explosive volcanic eruptions inject sulphur species to the stratosphere in the form of SO2 and H2S which convert to H2SO4 aerosols. To be more specific to the current study, the major observational sources of atmospheric (upper-air) temperature are basically common for all the reanalysis data sets in Table 1 (except for the 20CR which only assimilated surface pressure reports) They are radiosondes and satellite microwave and infrared sounders (i.e. MSU, SSU, and AMSU-A). Analysing all available reanalysis data sets for the 20th-century three major eruptions separately and for the region covering both troposphere and stratosphere will provide valuable information for model validation as well as on the current reanalysis data quality for capturing volcanic signals Such an analysis would be valuable when assessing one of the proposed geoengineering options, i.e. stratospheric aerosol injection to counteract global surface warming (e.g. Crutzen, 2006; Robock et al, 2013).

Data and method
The 1979–2009 analysis
The 1958–2001 Analysis
Conclusions
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