Detection and attribution of anthropogenic climate change impacts
Abstract
Human‐influenced climate change is an observed phenomenon affecting physical and biological systems across the globe. The majority of observed impacts are related to temperature changes and are located in the northern high‐ and mid‐latitudes. However, new evidence is emerging that demonstrates that impacts are related to precipitation changes as well as temperature, and that climate change is impacting systems and sectors beyond the Northern Hemisphere. In this paper, we highlight some of this new evidence—focusing on regions and sectors that the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) noted as under‐represented—in the context of observed climate change impacts, direct and indirect drivers of change (including carbon dioxide itself), and methods of detection. We also present methods and studies attributing observed impacts to anthropogenic forcing. We argue that the expansion of methods of detection (in terms of a broader array of climate variables and data sources, inclusion of the major modes of climate variability, and incorporation of other drivers of change) is key to discerning the climate sensitivities of sectors and systems in regions where the impacts of climate change currently remain elusive. Attributing such changes to human forcing of the climate system, where possible, is important for development of effective mitigation and adaptation. Current challenges in documenting adaptation and the role of indigenous knowledge in detection and attribution are described. WIREs Clim Change 2013, 4:121–150. doi: 10.1002/wcc.209 This article is categorized under: Assessing Impacts of Climate Change > Observed Impacts of Climate Change
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Identifying a robust climate change driver in systems subject to other anthropogenic interference requires careful assessment of other socio-economic factors.
“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 [42]
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“…required to produce robust evidence linking climate impacts to human migration (and health), how these data are accounted for in evidence synthesis and the basis on which to link data demonstrating environmental changes to anthropogenic climate change (Rosenzweig and Neofotis 2013, Ebi et al 2017).”
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- Authors
- Rosenzweig, C., & Neofotis, P.
- Affiliations
- Goddard Institute for Space Studies (US)
- The Graduate Center, CUNY (US)
- Corresponding author
- Rosenzweig, Cynthia
- Venue
- WIREs Climate Change
- Publisher
- Wiley
- Published
- 2013-02-26
- Volume
- 4
- Issue
- 2
- Pages
- 121-150
- ISSN
- 1757-7780 (ISSN-L)
- 1757-7799
- OpenAlex
- W1964126871
- Cited by
- 99
- References
- 208
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 9 · 2023: 9 · 2022: 7 · 2021: 7 · 2020: 8 · 2019: 2 · 2018: 11 · 2017: 3 · 2016: 7 · 2015: 11 · 2014: 5 · 2013: 2
- FWCI
- 6.34
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Wiley Interdisciplinary Reviews Climate Change (publishedVersion)
- NASA STI Repository (National Aeronautics and Space Administration) (submittedVersion)
- 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
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Geology and Paleoclimatology Research
- Keywords
- anthropogenic climate change, climate change impacts, climate change attribution, precipitation change, temperature change, climate variability, carbon dioxide, climate sensitivity, adaptation, indigenous knowledge, IPCC AR4
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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