Climate Dynamics

, Volume 52, Issue 5–6, pp 2667–2684 | Cite as

Does global warming amplify interannual climate variability?

  • Chao He
  • Tim LiEmail author


Based on the outputs of 30 models from Coupled Model Intercomparison Project Phase 5 (CMIP5), the fractional changes in the amplitude interannual variability (σ) for precipitation (P′) and vertical velocity (ω′) are assessed, and simple theoretical models are constructed to quantitatively understand the changes in σ(P′) and σ(ω′). Both RCP8.5 and RCP4.5 scenarios show similar results in term of the fractional change per degree of warming, with slightly lower inter-model uncertainty under RCP8.5. Based on the multi-model median, σ(P′) generally increases but σ(ω′) generally decreases under global warming but both are characterized by non-uniform spatial patterns. The σ(P′) decrease over subtropical subsidence regions but increase elsewhere, with a regional averaged value of 1.4% K− 1 over 20°S–50°N under RCP8.5. Diagnoses show that the mechanisms for the change in σ(P′) are different for climatological ascending and descending regions. Over ascending regions, the increase of mean state specific humidity contributes to a general increase of σ(P′) but the change of σ(ω′) dominates its spatial pattern and inter-model uncertainty. But over descending regions, the change of σ(P′) and its inter-model uncertainty are constrained by the change of mean state precipitation. The σ(ω′) is projected to be weakened almost everywhere except over equatorial Pacific, with a regional averaged fractional change of − 3.4% K− 1 at 500 hPa. The overall  reduction of σ(ω′) results from the increased mean state static stability, while the substantially increased σ(ω′) at the mid-upper troposphere over equatorial Pacific and the inter-model uncertainty of the changes in σ(ω′) are dominated by the change in the interannual variability of diabatic heating.


Global warming Interannual variability Precipitation Vertical velocity 



This work was supported by National Key Research and Development Program of China (2017YFA0604601), the National Natural Science Foundation of China (41505067), the United States National Science Foundation (AGS-1565653) and Open Research Fund Program of Key Laboratory of Meteorological Disaster of Ministry of Education (KLME1601). The authors wish to acknowledge the modeling groups and PCMDI for providing modeling data.

Supplementary material

382_2018_4286_MOESM1_ESM.pdf (5.3 mb)
Supplementary material 1 (PDF 5432 KB)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute for Environmental and Climate ResearchJinan UniversityGuangzhouChina
  2. 2.Key Laboratory of Meteorological Disaster of Ministry of EducationNanjing University of Information Science and TechnologyNanjingChina
  3. 3.International Pacific Research Center and Department of Atmospheric SciencesUniversity of HawaiiHonoluluUSA

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