Submitted as: model experiment description paper 30 Jun 2020

Submitted as: model experiment description paper | 30 Jun 2020

Review status: a revised version of this preprint is currently under review for the journal GMD.

ATTRICI 1.0 – counterfactual climate for impact attribution

Matthias Mengel, Simon Treu, Stefan Lange, and Katja Frieler Matthias Mengel et al.
  • Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, D-14412 Potsdam, Germany

Abstract. Climate has changed over the past century due to anthropogenic greenhouse gas emissions. In parallel, societies and their environment have evolved rapidly. To identify the impacts of historical climate change on human or natural systems, it is therefore necessary to separate the effect of different drivers. By definition this is done by comparing the observed situation to a counterfactual one in which climate change is absent and other drivers change according to observations. As such a counterfactual baseline cannot be observed it has to be estimated by process-based or empirical models. We here present ATTRICI (ATTRIbuting Climate Impacts), an approach to remove the signal of global warming from observational climate data to generate forcing data for the simulation of a counterfactual baseline of impact indicators. Our method identifies the interannual and annual cycle shifts that are correlated to global mean temperature change. We use quantile mapping to a baseline distribution that removes the global mean temperature related shifts to find counterfactual values for the observed daily climate data. Applied to each variable of two climate datasets, we produce two counterfactual datasets that are made available through the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) along with the original datasets. Our method preserves the internal variability of the observed data in the sense that observed (factual) and counterfactual data for a given day remain in the same quantile in their respective statistical distribution. That makes it possible to compare observed impact events and counterfactual impact events. Our approach adjusts for the long-term trends associated with global warming but does not address the attribution of climate change to anthropogenic greenhouse gas emissions.

Matthias Mengel et al.

Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Login for authors/topical editors] [Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement

Matthias Mengel et al.

Model code and software

Code to produce presented results M. Mengel and S. Treu

Matthias Mengel et al.


Total article views: 485 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
306 153 26 485 35 38 37
  • HTML: 306
  • PDF: 153
  • XML: 26
  • Total: 485
  • Supplement: 35
  • BibTeX: 38
  • EndNote: 37
Views and downloads (calculated since 30 Jun 2020)
Cumulative views and downloads (calculated since 30 Jun 2020)

Viewed (geographical distribution)

Total article views: 396 (including HTML, PDF, and XML) Thereof 396 with geography defined and 0 with unknown origin.
Country # Views %
  • 1


Latest update: 12 Jun 2021
Short summary
To identify the impacts of historical climate change it is necessary to separate the effect of the different impact drivers. To address this, one needs to compare historical impacts to a counterfactual world with impacts that would have been without climate change. We here present an approach that produces counterfactual climate data and can be used in climate impact models to simulate counterfactual impacts. We make this data available through the ISIMIP project.