The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., et al. (2020) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.3803 · Cited by 31,882
Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L. V., Rowell, D. P., Kent, E. C., & Kaplan, A. (2003) · Journal of Geophysical Research Atmospheres · doi:10.1029/2002jd002670 · Cited by 11,133
Updated high‐resolution grids of monthly climatic observations – the CRU TS3 .10 Dataset
Harris, I. C., Jones, P. D., Osborn, T. J., & Lister, D. (2013) · International Journal of Climatology · doi:10.1002/joc.3711 · Cited by 6,326
Climate Trends and Global Crop Production Since 1980
Lobell, D. B., Schlenker, W., & Costa-Roberts, J. (2011) · Science · doi:10.1126/science.1204531 · Cited by 4,927
IPCC (2022) · Cambridge University Press eBooks · doi:10.1017/9781009157940 · Cited by 3,909
The Twentieth Century Reanalysis Project
Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J., Yin, X., Gleason, B. E., Vose, R. S., Rutledge, G., Bessemoulin, P., et al. (2011) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.776 · Cited by 3,518
Impact of anthropogenic climate change on wildfire across western US forests
Abatzoglou, J. T., & Williams, A. P. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1607171113 · Cited by 3,073
Beta regression for modelling rates and proportions
Ferrari, S., & Cribari-Neto, F. (2004) · Journal of Applied Statistics · doi:10.1080/0266476042000214501 · Cited by 3,055
Probabilistic programming in Python using PyMC3
Salvatier, J., Wiecki, T. V., & Fonnesbeck, C. (2016) · PeerJ Computer Science · doi:10.7717/peerj-cs.55 · Cited by 2,544
A meta-analysis of crop yield under climate change and adaptation
Challinor, A. J., Watson, J., Lobell, D. B., Howden, S. M., Smith, D. R., & Chhetri, N. (2014) · Nature Climate Change · doi:10.1038/nclimate2153 · Cited by 2,333
A high-resolution data set of surface climate over global land areas
New, M. G., Lister, D., Hulme, M., & Makin, I. W. (2002) · Climate Research · doi:10.3354/cr021001 · Cited by 2,249
ADVANCED SPECTRAL METHODS FOR CLIMATIC TIME SERIES
Ghil, M., Allen, M. R., Dettinger, M. D., Ide, K., Kondrashov, D., MANN, M. E., Robertson, A. W., Saunders, A., Tian, Y. Z., Váradi, F., et al. (2002) · Reviews of Geophysics · doi:10.1029/2000rg000092 · Cited by 2,209
Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs
Wood, A. W., Leung, L.-Y. R., Sridhar, V., & Lettenmaier, D. P. (2004) · Climatic Change · doi:10.1023/b:clim.0000013685.99609.9e · Cited by 1,889
Cannon, A. J., Sobie, S. R., & Murdock, T. Q. (2015) · Journal of Climate · doi:10.1175/jcli-d-14-00754.1 · Cited by 1,700
Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Ziese, M., & Rudolf, B. (2014) · Theoretical and Applied Climatology · doi:10.1007/s00704-013-0860-x · Cited by 1,385
Gudmundsson, L., Bremnes, J. B., Haugen, J. E., & Engen-Skaugen, T. (2012) · Hydrology and Earth System Sciences · doi:10.5194/hess-16-3383-2012 · Cited by 1,318
Weedon, G. P., Balsamo, G., Bellouin, N., Gomes, S., Best, M. J., & Viterbo, P. (2014) · Water Resources Research · doi:10.1002/2014wr015638 · Cited by 1,072
Attribution of climate extreme events
Trenberth, K. E., Fasullo, J. T., & Shepherd, T. G. (2015) · Nature Climate Change · doi:10.1038/nclimate2657 · Cited by 982
Global warming has increased global economic inequality
Diffenbaugh, N. S., & Burke, M. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1816020116 · Cited by 927
Anthropogenic greenhouse gas contribution to flood risk in England and Wales in autumn 2000
Pall, P., Aina, T., Stone, D. A., Stott, P. A., Nozawa, T., Hilberts, A. G. J., Lohmann, D., & Allen, M. R. (2011) · Nature · doi:10.1038/nature09762 · Cited by 871
Piani, C., Weedon, G. P., Best, M., Gomes, S. M., Viterbo, P., Hagemann, S., & Haerter, J. O. (2010) · Journal of Hydrology · doi:10.1016/j.jhydrol.2010.10.024 · Cited by 868
Quantifying the influence of global warming on unprecedented extreme climate events
Diffenbaugh, N. S., Singh, D., Mankin, J., Horton, D. E., Swain, D. L., Touma, D., Charland, A., Liu, Y., Haugen, M. A., Tsiang, M., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1618082114 · Cited by 810
Attribution of extreme weather and climate‐related events
Stott, P. A., Christidis, N., Otto, F. E. L., Sun, Y., Vanderlinden, J., van Oldenborgh, G. J., Vautard, R., von Storch, H., Walton, P., Yiou, P., et al. (2016) · WIREs Climate Change · doi:10.1002/wcc.380 · Cited by 781
Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0)
Lange, S. (2019) · Geoscientific model development · doi:10.5194/gmd-12-3055-2019 · Cited by 745
Self-citationWFDE5: bias-adjusted ERA5 reanalysis data for impact studies
Cucchi, M., Weedon, G. P., Amici, A., Bellouin, N., Lange, S., Schmied, H. M., Hersbach, H., & Buontempo, C. (2020) · Earth system science data · doi:10.5194/essd-12-2097-2020 · Cited by 602
Self-citationThe Detection and Attribution Model Intercomparison Project (DAMIP v1.0) contribution to CMIP6
Gillett, N. P., Shiogama, H., Funke, B., Hegerl, G., Knutti, R., Matthes, K., Santer, B. D., Stone, D., & Tebaldi, C. (2016) · Geoscientific Model Development · doi:10.5194/gmd-9-3685-2016 · Cited by 597
Water scarcity hotspots travel downstream due to human interventions in the 20th and 21st century
Veldkamp, T., Wada, Y., Aerts, J. C. J. H., Döll, P., Gosling, S. N., Liu, J., Masaki, Y., Oki, T., Ostberg, S., POKHREL, Y. N., et al. (2017) · Nature Communications · doi:10.1038/ncomms15697 · Cited by 520
Attributing human mortality during extreme heat waves to anthropogenic climate change
Mitchell, D., Heaviside, C., Vardoulakis, S., Huntingford, C., Masato, G., P Guillod, B., Frumhoff, P., Bowery, A., Wallom, D., & Allen, M. (2016) · Environmental Research Letters · doi:10.1088/1748-9326/11/7/074006 · Cited by 439
Pattern Scaling: An Examination of the Accuracy of the Technique for Describing Future Climates
Mitchell, T. D. (2003) · Climatic Change · doi:10.1023/a:1026035305597 · Cited by 397
Observed changes in dry-season water availability attributed to human-induced climate change
Padrón, R. S., Gudmundsson, L., Decharme, B., Ducharne, A., Lawrence, D. M., Mao, J., Peano, D., Krinner, G., Kim, H., & Seneviratne, S. I. (2020) · Nature Geoscience · doi:10.1038/s41561-020-0594-1 · Cited by 330
State-of-the-art global models underestimate impacts from climate extremes
Schewe, J., Gosling, S. N., Reyer, C., Zhao, F., Ciais, P., Elliott, J. W., François, L., Huber, V., Lotze, H. K., Seneviratne, S. I., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08745-6 · Cited by 329
Self-citationHuman influence on climate in the 2014 southern England winter floods and their impacts
Schaller, N., Kay, A. L., Lamb, R., Massey, N., van Oldenborgh, G. J., Otto, F. E. L., Sparrow, S., Vautard, R., Yiou, P., Ashpole, I., et al. (2016) · Nature Climate Change · doi:10.1038/nclimate2927 · Cited by 323
van den Hurk, B., Kim, H., Krinner, G., Seneviratne, S. I., Derksen, C., Oki, T., Douville, H., Colin, J., Ducharne, A., Cheruy, F., et al. (2016) · Geoscientific model development · doi:10.5194/gmd-9-2809-2016 · Cited by 317
Evaluating uncertainties in the prediction of regional climate change
Giorgi, F., & Francisco, R. (2000) · Geophysical Research Letters · doi:10.1029/1999gl011016 · Cited by 304
Veldkamp, T., Zhao, F., Ward, P. J., de Moel, H., Aerts, J. C. J. H., Schmied, H. M., Portmann, F. T., Masaki, Y., POKHREL, Y. N., Liu, X., et al. (2018) · Environmental Research Letters · doi:10.1088/1748-9326/aab96f · Cited by 282
Attribution of Extreme Weather Events in the Context of Climate Change
Stott, P. A., Christidis, N., Otto, F. E. L., Sun, Y., Vanderlinden, J., van Oldenborgh, G. J., Vautard, R., von STORCH, H., Walton, P., Yiou, P., et al. (2016) · HAL · doi:10.17226/21852 · Cited by 266
ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks
Krinner, G., Derksen, C., Essery, R., Flanner, M. G., Hagemann, S., Clark, M., Hall, A. D., Rott, H., Brutel-Vuilmet, C., Kim, H., et al. (2018) · Geoscientific model development · doi:10.5194/gmd-11-5027-2018 · Cited by 264
Developing climate scenarios from equilibrium GCM results
Santer, B. D., Wigley, T. M. L., Schlesinger, M. E., & Mitchell, J. F. B. (1990) · MPG.PuRe (Max Planck Society) · Cited by 251
Schmied, H. M., Adam, L., Eisner, S., Fink, G., Flörke, M., Kim, H., Oki, T., Portmann, F. T., Reinecke, R., Riedel, C., et al. (2016) · Hydrology and earth system sciences · doi:10.5194/hess-20-2877-2016 · Cited by 232
Chen, L., & Frauenfeld, O. W. (2014) · Journal of Geophysical Research Atmospheres · doi:10.1002/2013jd021190 · Cited by 216
Wang, X. L., Feng, Y., Compo, G. P., Swail, V. R., Zwiers, F. W., Allan, R. J., & Sardeshmukh, P. D. (2012) · Climate Dynamics · doi:10.1007/s00382-012-1450-9 · Cited by 170
The economic costs of Hurricane Harvey attributable to climate change
Frame, D. J., Wehner, M. F., Noy, I., & Rosier, S. M. (2020) · Climatic Change · doi:10.1007/s10584-020-02692-8 · Cited by 142
Global Soil Wetness Project Phase 3 Atmospheric Boundary Conditions (Experiment 1)
Kim, H. (2017) · Repository for Data Integration and Analysis System · doi:10.20783/dias.501 · Cited by 138
Peculiarly pleasant weather for US maize
Butler, E. E., Mueller, N. D., & Huybers, P. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1808035115 · Cited by 137
Hof, C., Voskamp, A., Biber, M. F., Böhning-Gaese, K., Engelhardt, E. K., Niamir, A., Willis, S. G., & Hickler, T. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1807745115 · Cited by 131
Improved pattern scaling approaches for the use in climate impact studies
Herger, N., Sanderson, B. M., & Knutti, R. (2015) · Geophysical Research Letters · doi:10.1002/2015gl063569 · Cited by 129
Good practice guidance paper on detection and attribution related to anthropogenic climate change
Hegerl, G. C., Hoegh‐Guldberg, O., Gino Casassa, R., Hoerling, M. P., Kovats, S. R., Parmesan, C., Pierce, D. W., & Stott, P. A. (2010) · Cited by 127
Dynamical Global Downscaling of Global Reanalysis
Yoshimura, K., & Kanamitsu, M. (2008) · Monthly Weather Review · doi:10.1175/2008mwr2281.1 · Cited by 124
Iizumi, T., Shiogama, H., Imada, Y., Hanasaki, N., Takikawa, H., & Nishimori, M. (2018) · International Journal of Climatology · doi:10.1002/joc.5818 · Cited by 123
WFDE5 over land merged with ERA5 over the ocean (W5E5 v2.0)
Lange, S., Menz, C., Gleixner, S., Cucchi, M., Weedon, G. P., Amici, A., Bellouin, N., Müller Schmied, H., Hersbach, H., Buontempo, C., et al. (2021) · Publication Database PIK (Potsdam Institute for Climate Impact Research (PIK)) · doi:10.48364/isimip.342217 · Cited by 119
Self-citationZhu, P., Zhuang, Q., Archontoulis, S. V., Bernacchi, C. J., & Müller, C. (2019) · Global Change Biology · doi:10.1111/gcb.14632 · Cited by 107
Minoli, S., Müller, C., Elliott, J. W., Ruane, A. C., Jägermeyr, J., Zabel, F., Dury, M., Folberth, C., François, L., Hank, T., et al. (2019) · Earth s Future · doi:10.1029/2018ef001130 · Cited by 98
Climate signals in river flood damages emerge under sound regional disaggregation
Sauer, I., Reese, R., Otto, C., Geiger, T., Willner, S. N., Guillod, B. P., Bresch, D. N., & Frieler, K. (2021) · Nature Communications · doi:10.1038/s41467-021-22153-9 · Cited by 79
Self-citationLange, S. (2019) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.2549631 · Cited by 75
Self-citationModeling the Impact of Summer Temperatures on National Electricity Consumption
Comte, D. M. L., & Warren, H. E. (1981) · Journal of applied meteorology · doi:10.1175/1520-0450(1981)020 · Cited by 73
Benchmarking carbon fluxes of the ISIMIP2a biome models
Chang, J., Ciais, P., Wang, X., Piao, S. L., Asrar, G., Betts, R., Chevallier, F., Dury, M., François, L., Frieler, K., et al. (2017) · Environmental Research Letters · doi:10.1088/1748-9326/aa63fa · Cited by 48
Self-citationSpectral nudging sensitivity experiments in a regional climate model
Hong, S., & Chang, E. (2012) · Asia-Pacific Journal of Atmospheric Sciences · doi:10.1007/s13143-012-0033-3 · Cited by 47
Tokuda, D., Kim, H., Yamazaki, D., & Oki, T. (2019) · Water Resources Research · doi:10.1029/2018wr023083 · Cited by 47
The Law and Science of Climate Change Attribution
Burger, M., Wentz, J. A., & Horton, R. (2020) · Columbia Journal of Environmental Law · doi:10.7916/cjel.v45i1.4730 · Cited by 43
Acts of God, human influence and litigation
Marjanac, S., Patton, L., & Thornton, J. (2017) · Nature Geoscience · doi:10.1038/ngeo3019 · Cited by 39
Tangdamrongsub, N., Han, S., Decker, M., Yeo, I., & Kim, H. (2018) · Hydrology and earth system sciences · doi:10.5194/hess-22-1811-2018 · Cited by 34
Incremental Correction for the Dynamical Downscaling of Ensemble Mean Atmospheric Fields
Yoshimura, K., & Kanamitsu, M. (2013) · Monthly Weather Review · doi:10.1175/mwr-d-12-00271.1 · Cited by 27
Visualizion code for ATTRICI 1.0
Mengel, M., & Treu, S. (2020) · Figshare · doi:10.5281/zenodo.3829132 · Cited by 1
Self-citationISI-MIP/attrici: Code as used for manuscript submission to GMD
Mengel, M., SimonTreu, Schmidt, B., Gieseke, R., & Willner, S. N. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.3828914 · Cited by 1
Self-citationVisualizion code for ATTRICI 1.1
Mengel, M., & Treu, S. (2021) · Zenodo · doi:10.5281/zenodo.5036701
Self-citationISI-MIP/attrici: Major methodological updates for final manuscript
Mengel, M., & Schmidt, B. (2021) · Zenodo · doi:10.5281/zenodo.5032065
Self-citationData from ATTRICI 1.1 - counterfactual climate for impact attribution
Mengel, M., Treu, S., Lange, S., & Frieler, K. (2021) · Zenodo · doi:10.5281/zenodo.5036364
Self-citationLange, S. (2021) · Zenodo · doi:10.5281/zenodo.4686991
Self-citation
Research works printed but not linked: 1
- IPCC: Climate Change 2014 – Impacts, Adaptation and Vulnerability: Global and Sectoral Aspects, Cambridge University Press, 1140 pp., 2014.
Cited by
Sorted by citations
Global river water quality under climate change and hydroclimatic extremes
van Vliet, M. T. H., Thorslund, J., Strokal, M., Hofstra, N., Flörke, M., Macedo, H. E., Nkwasa, A., Tang, T., Kaushal, S. S., Kumar, R., et al. (2023) · Nature Reviews Earth & Environment · doi:10.1038/s43017-023-00472-3 · Cited by 545
Jones, M. W., Kelley, D. I., Burton, C., Giuseppe, F. D., Barbosa, M. L. F., Brambleby, E., Hartley, A. J., Lombardi, A., Mataveli, G., McNorton, J., et al. (2024) · Earth system science data · doi:10.5194/essd-16-3601-2024 · Cited by 339
Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies
Callaghan, M., Schleussner, C.-F., Nath, S., Lejeune, Q., Knutson, T. R., Reichstein, M., Hansen, G., Theokritoff, E., Andrijevic, M., Brecha, R. J., et al. (2021) · Nature Climate Change · doi:10.1038/s41558-021-01168-6 · Cited by 251
Frieler, K., Volkholz, J., Lange, S., Schewe, J., Mengel, M., López, M. D. R. R., Otto, C., Reyer, C., Karger, D. N., Malle, J., et al. (2024) · Geoscientific model development · doi:10.5194/gmd-17-1-2024 · Cited by 191
Self-citationGlobal burned area increasingly explained by climate change
Burton, C., Lampe, S., Kelley, D. I., Thiery, W., Hantson, S., Christidis, N., Gudmundsson, L., Forrest, M., Burke, E., Chang, J., et al. (2024) · Nature Climate Change · doi:10.1038/s41558-024-02140-w · Cited by 179
Self-citation
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
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 ›
Are you an author, or do you see a problem with this page? Request a review
