13 Jul 2022
13 Jul 2022

Robust global detection of forced changes in mean and extreme precipitation despite observational disagreement on the magnitude of change

Iris Elisabeth de Vries1, Sebastian Sippel1, Angeline Greene Pendergrass2,3, and Reto Knutti1 Iris Elisabeth de Vries et al.
  • 1Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Zurich, Switzerland
  • 2Earth and Atmospheric Sciences, Cornell University, Ithaca, NY, USA
  • 3National Center for Atmospheric Research, Boulder, CO, USA

Abstract. Detection and attribution (D&A) of forced precipitation change is challenging due to internal variability and limited spatial and temporal coverage of observational records. These factors result in a low signal-to-noise ratio of potential regional and even global trends. Here, we use a statistical method – ridge regression – to create physically interpretable fingerprints for detection of forced changes in mean and extreme precipitation with a high signal-to-noise ratio. The fingerprints are constructed using CMIP6 multi-model output masked to match coverage of three gridded precipitation observational datasets – GHCNDEX, HadEX3, and GPCC –, and are then applied to these observational datasets to assess the degree of forced change detectable in the real-world climate.

We show that the signature of forced change is detected in all three observational datasets for global metrics of mean and extreme precipitation. Forced changes are still detectable from changes in the spatial patterns of precipitation even if the global mean trend is removed from the data. This shows detection of forced change in mean and extreme precipitation beyond a global mean trend, and increases confidence in the detection method's power, as well as in climate models' ability to capture the relevant processes that contribute to large-scale patterns of change.

We also find, however, that detectability depends on the observational dataset used. Not only coverage differences but also observational uncertainty contribute to dataset disagreement, exemplified by times of emergence of forced change from internal variability ranging from 1998 to 2004 among datasets. Furthermore, different choices for the period over which the forced trend is computed result in different levels of agreement between observations and model projections. These sensitivities may explain apparent contradictions in recent studies on whether models under- or overestimate the observed forced increase in mean and extreme precipitation. Lastly, the detection fingerprints are found to rely primarily on the signal in the extratropical Northern Hemisphere, which is at least partly due to observational coverage, but potentially also due to the presence of a more robust signal in the Northern Hemisphere in general.

Iris Elisabeth de Vries et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-568', Anonymous Referee #1, 02 Sep 2022
    • AC1: 'Reply on RC1', Iris de Vries, 08 Sep 2022
  • RC2: 'Comment on egusphere-2022-568', Anonymous Referee #2, 06 Sep 2022
    • AC2: 'Reply on RC2', Iris de Vries, 12 Oct 2022

Iris Elisabeth de Vries et al.

Iris Elisabeth de Vries et al.


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Short summary
Precipitation changes are an important consequence of climate change. We use a method based on models and observations to detect changes in mean and extreme precipitation caused by external influence. We detect forced change in three observational datasets for global mean and extreme precipitation, but different observational datasets show different magnitudes of forced change. It is thus uncertain how large the change is, which is important for prediction of future changes and adaptation.