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ATTRICI v1.1 – counterfactual climate for impact attribution
Open accessPublished version of a preprint
Abstract
Attribution in its general definition aims to quantify drivers of change in a system. According to IPCC Working Group II (WGII) a change in a natural, human or managed system is attributed to climate change by quantifying the difference between the observed state of the system and a counterfactual baseline that characterizes the system's behavior in the absence of climate change, where “climate change refers to any long-term trend in climate, irrespective of its cause” (IPCC, 2014). Impact attribution following this definition remains a challenge because the counterfactual baseline, which characterizes the system behavior in the hypothetical absence of climate change, cannot be observed. Process-based and empirical impact models can fill this gap as they allow us to simulate the counterfactual climate impact baseline. In those simulations, the models are forced by observed direct (human) drivers such as land use changes, changes in water or agricultural management but a counterfactual climate without long-term changes. We here present ATTRICI (ATTRIbuting Climate Impacts), an approach to construct the required counterfactual stationary climate data from observational (factual) climate data. Our method identifies the long-term shifts in the considered daily climate variables that are correlated to global mean temperature change assuming a smooth annual cycle of the associated scaling coefficients for each day of the year. The produced counterfactual climate datasets are used as forcing data within the impact attribution setup of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a). Our method preserves the internal variability of the observed data in the sense that factual and counterfactual data for a given day have the same rank in their respective statistical distributions. The associated impact model simulations allow for quantifying the contribution of climate change to observed long-term changes in impact indicators and for quantifying the contribution of the observed trend in climate to the magnitude of individual impact events. Attribution of climate impacts to anthropogenic forcing would need an additional step separating anthropogenic climate forcing from other sources of climate trends, which is not covered by our method.
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“In the case of systems subject to other anthropogenic interference such as the global biosphere, managed systems such as agriculture, or human systems themselves, identifying a robust climate change driver requires careful assessment of other socio-economic factors 41,42 , adding additional levels of complexity 43 .”
From Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies (2021) · cites this paper as [43]
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Metadata
- Authors
- Mengel, M., Treu, S., Lange, S., & Frieler, K.
- Affiliations
- Leibniz Association (DE)
- Corresponding author
- Mengel, Matthias
- Treu, Simon
- Lange, Stefan
- Venue
- Geoscientific Model Development
- Publisher
- Copernicus GmbH
- Published
- 2021-08-20
- Volume
- 14
- Issue
- 8
- Pages
- 5269-5284
- ISSN
- 1991-959X (ISSN-L)
- 1991-9603
- OpenAlex
- W3092205005
- Cited by
- 119
- References
- 66
- Citations by year
- 2026: 17 · 2025: 27 · 2024: 35 · 2023: 23 · 2022: 11 · 2021: 6
- FWCI
- 7.69
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Geoscientific model development (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- RENATE (submittedVersion, cc-by-nc-nd)
- Publication Database PIK (Potsdam Institute for Climate Impact Research (PIK)) (submittedVersion, cc-by)
- RENATE (cc-by)
- Funders
- European Commission (641816, 820712, HORIZON2020, H2020)
- Bundesministerium für Bildung und Forschung (01LS1711A)
- Horizon 2020 Framework Programme (820712, 641816)
- Leibniz-Gemeinschaft
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate Change and Environmental Impact
- Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Climate change impacts on agriculture
- Keywords
- long-term climate trends, global mean temperature, ISIMIP3a, IMPACT model, natural climate variability, anthropogenic climate change
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