Abstract

Extreme temperatures affect populous regions, like eastern China, causing substantial socio-economic losses. It is beneficial to explore whether the frequencies of absolute or threshold-based extreme temperatures have been changed by human activities, such as anthropogenic emissions of greenhouse gases (GHGs). In this study, we compared observed and multi-model-simulated changes in the frequencies of summer days, tropical nights, icy days and frosty nights in eastern China for the years 1960–2012 by using an optimal fingerprinting method. The observed long-term trends in the regional mean frequencies of these four indices were +2.36, +1.62, −0.94, −3.02 days decade−1. The models performed better in simulating the observed frequency change in daytime extreme temperatures than nighttime ones. Anthropogenic influences are detectable in the observed frequency changes of these four temperature extreme indices. The influence of natural forcings could not be detected robustly in any indices. Further analysis found that the effects of GHGs changed the frequencies of summer days (tropical nights, icy days, frosty nights) by +3.48 ± 1.45 (+2.99 ± 1.35, −2.52 ± 1.28, −4.11 ± 1.48) days decade−1. Other anthropogenic forcing agents (dominated by anthropogenic aerosols) offset the GHG effect and changed the frequencies of these four indices by −1.53 ± 0.78, −1.49 ± 0.94, +1.84 ± 1.07, +1.45 ± 1.26 days decade−1, respectively. Little influence of natural forcings was found in the observed frequency changes of these four temperature extreme indices.

Highlights

  • Extreme temperatures cause substantial risk to human health, agriculture and the ecosystem (Field et al 2012)

  • One-signal analysis suggests that climate models with ALL forcing can reproduce the observed frequency changes in summer days and icy days well, and have scaling factor estimates consistent with the value one though bear certain internal variability

  • We find that the observed frequency changes in extreme temperatures are the net result of the counteracting effects from greenhouse gases (GHGs) and OANT forcing agents, since natural forcing only (NAT) forcing imposes little influence on these changes

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Summary

10 January 2018

Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Keywords: extreme temperatures, human activities, greenhouse gases, detection and attribution

Introduction
Observations We use a newly homogenized daily Tmax and
Model simulations
Optimal fingerprinting method
Patterns and one-signal detection analysis
Two-signal detection analysis
Three-signal detection analysis
Attribution
Robustness test
Summary

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