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arxiv: 2408.16004 · v3 · submitted 2024-08-13 · 📊 stat.AP

Granger causal inference for climate change attribution

Mark D. Risser , Mohammed Ombadi , Michael F. Wehner This is my paper

Pith reviewed 2026-05-23 22:13 UTC · model grok-4.3

classification 📊 stat.AP
keywords climate change attributionGranger causalityPearl causalitycausal inferenceextreme weather eventsdetection and attributionanthropogenic influence
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The pith

Granger causality permits attribution of climate trends and events using observational data alone.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper defines Granger-based methods for attributing trends and extreme events in the climate system to anthropogenic activities. It contrasts these predictive approaches with traditional Pearl-causal methods that rely on dynamical model experiments. The authors show that Granger methods require only observational records and support rapid attribution after weather events occur. They argue that combining both causal perspectives strengthens overall confidence in human influence on climate without introducing new conditional biases.

Core claim

Granger-causal attribution statements can be constructed quickly from observations and do not require computationally-intensive dynamical experiments. These analyses also enable rapid attribution, which is useful in the aftermath of a severe weather event, and provide multiple lines of evidence for anthropogenic climate change when paired with Pearl-causal attribution.

What carries the argument

Granger causality for attribution, where past values of anthropogenic forcings improve prediction of climate variables beyond autoregressive terms alone.

If this is right

  • Attribution analyses can be performed rapidly using existing observations without running new model simulations.
  • Rapid attribution statements become feasible immediately after severe weather events.
  • Confidence in overall attribution conclusions increases when Granger and Pearl methods reach similar results.
  • Hybrid approaches that leverage both causal frameworks are feasible for the detection and attribution community.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Granger methods could extend attribution coverage to more events and variables where full dynamical model experiments remain unavailable.
  • The observational basis may permit more frequent re-evaluation of attribution statements as new data accumulates.

Load-bearing premise

That a formal definition of Granger-based trend and event attribution exists which is structurally distinct from Pearl-causal methods, yields interpretable attribution conclusions, and can be combined with them without introducing new conditional biases.

What would settle it

A well-studied extreme event where Granger-based attribution statements systematically disagree in sign or magnitude with established Pearl-causal attributions.

Figures

Figures reproduced from arXiv: 2408.16004 by Mark D. Risser, Michael F. Wehner, Mohammed Ombadi.

Figure 1
Figure 1. Figure 1: A schematic overview of the commonly-used approaches to climate change Detection & Attribution, considering both trend and event attribution methods. Each method is categorized according to whether it utilizes Pearl or Granger causality. While in principle D&A methods can be applied to changes in spatial patterns, shifts, or variability, in most cases the focus is on changes over time. The focus on time tr… view at source ↗
Figure 2
Figure 2. Figure 2: Causal graphs illustrating the relationship between the causal variable X, response or outcome variable Y , and covariate(s) Z used in Equations 1 and 2. In all panels, solid lines indicate the causal relationship being tested, while the dashed lines represent additional causal relationships within the system. Panel (a) shows the hypothesis that global warming (X) causes an increase in the intensity of ext… view at source ↗
Figure 3
Figure 3. Figure 3: Input data for the statistical counterfactual analysis described in Section 4. Panel (a) shows global mean surface temperature anomalies (◦C) relative to 1950-1980 (from Lenssen et al., 2019); panel (b) shows radiative forcing (W m−2 ) from greenhouse gas forcing, anthropogenic aerosols, and the combined anthropogenic forcing; panel (c) shows stratospheric aerosol optical depth from volcanic activity; pane… view at source ↗
Figure 4
Figure 4. Figure 4: Reconstructing the effect of each covariate on the “original” scale, i.e., cumulatively summing the first-order differences. Trajectories include a “likely” (66%) confidence interval. “ALL” refers to all covariates, “ANT” is the combined anthropogenic radiative forcing, “GHG” and “AER” are the greenhouse gas and anthropogenic aerosol contributions (respectively; recall ANT = GHG + AER), “Volcanoes” represe… view at source ↗
Figure 5
Figure 5. Figure 5: Scenario-specific GMST distributions for the three counterfactual (scenarios 1, 2, and 4) and one “factual” (scenario 3) climate scenarios. The vertical dashed line shows the event of interest: a GMST anomaly of 0.9 ◦C. Also shown in shading (and labels) are the probabilities {Pj : j = 1, . . . , 4} of experiencing a GMST anomaly of at least 0.9 ◦C in each scenario [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗
read the original abstract

Climate change detection and attribution (D&A) is concerned with determining the extent to which anthropogenic activities have influenced specific aspects of the global climate system. D&A fits within the broader field of causal inference, the collection of statistical methods that identify cause and effect relationships. There are a wide variety of methods for making attribution statements, each of which require different types of input data and each of which are conditional to varying extents. Some methods are based on Pearl causality (experimental interference) while others leverage Granger (predictive) causality, and the causal framing provides important context for how the resulting attribution conclusion should be interpreted. However, while Granger-causal attribution analyses have become more common, there is no clear statement of their strengths and weaknesses and no clear consensus on where and when Granger-causal perspectives are appropriate. In this prospective paper, we provide a formal definition for Granger-based approaches to trend and event attribution and a clear comparison with more traditional methods for assessing the human influence on extreme weather and climate events. Broadly speaking, Granger-causal attribution statements can be constructed quickly from observations and do not require computationally-intesive dynamical experiments. These analyses also enable rapid attribution, which is useful in the aftermath of a severe weather event, and provide multiple lines of evidence for anthropogenic climate change when paired with Pearl-causal attribution. Confidence in attribution statements is increased when different methodologies arrive at similar conclusions. Moving forward, we encourage the D&A community to embrace hybrid approaches to climate change attribution that leverage the strengths of both Granger and Pearl causality.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript is a prospective paper that proposes a formal definition of Granger-causal methods for trend and event attribution in climate change detection and attribution (D&A). It contrasts these with Pearl-causal (interventional) approaches, argues that Granger-based statements can be constructed rapidly from observational time series without dynamical model experiments, enable post-event rapid attribution, and supply complementary lines of evidence when combined with Pearl-causal results. The central recommendation is that the D&A community adopt hybrid Granger-Pearl frameworks to increase overall confidence in anthropogenic influence statements.

Significance. If the claimed formal definitions prove internally consistent, distinct from Pearl causality, and free of new conditional biases when combined, the work could support faster observational attribution pipelines useful for real-time event analysis and provide a structured way to triangulate evidence across methodological families. The absence of any explicit derivations, theorems, or worked examples in the text, however, leaves the practical utility and robustness of these definitions untested.

major comments (2)
  1. [Abstract] Abstract (paragraph beginning 'Broadly speaking...'): The central claim that a Granger-based definition for trend/event attribution 'exists which is structurally distinct from Pearl-causal methods, yields interpretable attribution conclusions, and can be combined... without introducing new conditional biases' is asserted but not demonstrated. No explicit mathematical definition, set of axioms, or comparison result is supplied, so it is impossible to verify whether the definition reduces to Pearl-style assumptions or introduces new biases under non-stationarity and confounding typical of climate series.
  2. [Abstract] Abstract (final paragraph): The assertion that 'confidence in attribution statements is increased when different methodologies arrive at similar conclusions' is presented as a benefit of hybrid approaches, yet the text supplies no argument or counter-example showing that the conditional extents of Granger and Pearl statements are sufficiently orthogonal to avoid double-counting or spurious reinforcement under shared observational limitations.
minor comments (2)
  1. [Abstract] Abstract: 'computationally-intesive' is a typographical error and should read 'computationally-intensive'.
  2. [Abstract] Abstract: The term 'prospective paper' is used; if the intended meaning is 'perspective paper', this should be corrected for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on this prospective manuscript. We address each major comment below and indicate the revisions that will be incorporated.

read point-by-point responses

  1. Referee: [Abstract] Abstract (paragraph beginning 'Broadly speaking...'): The central claim that a Granger-based definition for trend/event attribution 'exists which is structurally distinct from Pearl-causal methods, yields interpretable attribution conclusions, and can be combined... without introducing new conditional biases' is asserted but not demonstrated. No explicit mathematical definition, set of axioms, or comparison result is supplied, so it is impossible to verify whether the definition reduces to Pearl-style assumptions or introduces new biases under non-stationarity and confounding typical of climate series.

    Authors: We agree that the abstract asserts the existence of a distinct Granger-based definition without supplying an explicit mathematical formulation or comparison. As the manuscript is prospective and intended to outline a conceptual framework rather than deliver a fully derived theory, we will revise the abstract to clarify its prospective nature. We will also add a dedicated section in the main text that provides a preliminary formal definition of Granger-causal attribution, states its key axioms, and contrasts it with Pearl-causal assumptions, including a brief discussion of behavior under non-stationarity. revision: yes

  2. Referee: [Abstract] Abstract (final paragraph): The assertion that 'confidence in attribution statements is increased when different methodologies arrive at similar conclusions' is presented as a benefit of hybrid approaches, yet the text supplies no argument or counter-example showing that the conditional extents of Granger and Pearl statements are sufficiently orthogonal to avoid double-counting or spurious reinforcement under shared observational limitations.

    Authors: The statement reflects a standard principle in multi-method causal inference. To meet the referee's request for an explicit argument, we will insert a new discussion paragraph that contrasts the identifying assumptions (Granger: incremental predictive power from lagged observations; Pearl: invariance under intervention) and notes that shared observational limitations can produce non-orthogonal statements. The paragraph will also acknowledge the risk of spurious reinforcement and recommend that hybrid use be accompanied by explicit checks for common confounders. revision: yes

Circularity Check

0 steps flagged

No circularity: prospective methodological comparison with no derivations or fitted quantities

full rationale

The paper is a prospective discussion piece that states it will provide a formal definition for Granger-based trend and event attribution and compare it to Pearl-causal methods. No equations, parameter fits, predictions, or derivation chains appear in the abstract or framing. The central claims rest on general statements about observational construction, rapid attribution, and hybrid evidence rather than any step that reduces an output to its own inputs by construction, self-citation load-bearing, or renaming. This matches the reader's assessment that the argument is a methodological comparison without self-referential reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on the applicability of standard Granger causality to climate time series and on the premise that agreement across causal frameworks increases evidential weight; both are imported from prior literature without new justification in the abstract.

axioms (2)
  • domain assumption Granger causality supplies valid attribution statements for climate trends and events when applied to observational time series
    The paper's proposal for formal definitions and rapid attribution presupposes this applicability.
  • domain assumption Agreement between Granger-causal and Pearl-causal conclusions increases overall confidence in anthropogenic attribution
    Stated directly in the abstract as a benefit of hybrid approaches.

pith-pipeline@v0.9.0 · 5804 in / 1322 out tokens · 33975 ms · 2026-05-23T22:13:50.542480+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

81 extracted references · 81 canonical work pages

  1. [1]

    Allen, M. R. and Stott, P. A. (2003). Estimating signal amplitudes in optimal fingerprinting, part I: theory . Climate Dynamics , 21(5–6):477–491

    work page 2003

  2. [2]

    Angélil, O., Stone, D., Wehner, M., Paciorek, C., Krishnan, H., and Collins, W. (2017). An independent assessment of anthropogenic attribution statements for recent extreme temperature and rainfall events. Journal of Climate , 30

    work page 2017

  3. [3]

    B., and Seth, A

    Barnett, L., Barrett, A. B., and Seth, A. K. (2009). Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables . Physical Review Letters , 103(23)

    work page 2009

  4. [4]

    P., Pierce, D

    Barnett, T. P., Pierce, D. W., AchutaRao, K. M., Gleckler, P. J., Santer, B. D., Gregory, J. M., and Washington, W. M. (2005). Penetration of human-induced warming into the world's oceans . Science , 309(5732):284--287

    work page 2005

  5. [5]

    A., Wehner, M

    Bercos‐Hickey, E., O’Brien, T. A., Wehner, M. F., Zhang, L., Patricola, C. M., Huang, H., and Risser, M. D. (2022). Anthropogenic Contributions to the 2021 Pacific Northwest Heatwave . Geophysical Research Letters , 49(23)

    work page 2022

  6. [6]

    and Stott, P

    Christidis, N. and Stott, P. A. (2022). Human Influence on Seasonal Precipitation in Europe . Journal of Climate , 35(15):5215--5231

    work page 2022

  7. [7]

    P., Stephenson, D

    Cummins, D. P., Stephenson, D. B., and Stott, P. A. (2022). Could detection and attribution of climate change trends be spurious regression? Climate Dynamics , 59:2785--2799

    work page 2022

  8. [8]

    DelSole, T., Trenary, L., Yan, X., and Tippett, M. K. (2019). Confidence intervals in optimal fingerprinting. Climate Dynamics , 52(7):4111--4126

    work page 2019

  9. [9]

    Easterling, D., Kunkel, K., Arnold, J., Knutson, T., LeGrande, A., Leung, L., Vose, R., Waliser, D., and Wehner, M. (2017). Precipitation change in the U nited S tates. In: Climate Science Special Report: Fourth National Climate Assessment, Volume I , pages 207--230

    work page 2017

  10. [10]

    A., Senior, C

    Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization . Geoscientific Model Development , 9(5):1937--1958

    work page 2016

  11. [11]

    Fisher, R. A. et al. (1960). The design of experiments. The design of experiments. , (7th Ed)

    work page 1960

  12. [12]

    J., Wehner, M

    Frame, D. J., Wehner, M. F., Noy, I., and Rosier, S. M. (2020). The economic costs of Hurricane Harvey attributable to climate change . Climatic Change , 160(2):271–281

    work page 2020

  13. [13]

    Gangl, M. (2010). Causal inference in sociological research. Annual Review of Sociology , 36(1):21–47

    work page 2010

  14. [14]

    P., Shiogama, H., Funke, B., Hegerl, G., Knutti, R., Matthes, K., Santer, B

    Gillett, N. P., Shiogama, H., Funke, B., Hegerl, G., Knutti, R., Matthes, K., Santer, B. D., Stone, D., and Tebaldi, C. (2016). The detection and attribution model intercomparison project ( DAMIP v1. 0) contribution to CMIP6 . Geoscientific Model Development , 9(10):3685--3697

    work page 2016

  15. [15]

    P., Zwiers, F

    Gillett, N. P., Zwiers, F. W., Weaver, A. J., and Stott, P. A. (2003). Detection of human influence on sea-level pressure. Nature , 422(6929):292--294

    work page 2003

  16. [16]

    Granger, C. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society , pages 424--438

    work page 1969

  17. [17]

    Hannart, A., Pearl, J., Otto, F., Naveau, P., and Ghil, M. (2015). Causal counterfactual theory for the attribution of weather and climate-related events . Bulletin of the American Meteorological Society , 97:99--110

    work page 2015

  18. [18]

    Hasselmann, K. (1979). On the signal-to-noise problem in atmospheric response studies

    work page 1979

  19. [19]

    Hasselmann, K. (1993). Optimal fingerprints for the detection of time-dependent climate change. Journal of Climate , 6(10):1957–1971

    work page 1993

  20. [20]

    C., Black, E., Allan, R

    Hegerl, G. C., Black, E., Allan, R. P., Ingram, W. J., Polson, D., Trenberth, K. E., Chadwick, R. S., Arkin, P. A., Sarojini, B. B., Becker, A., et al. (2015). Challenges in quantifying changes in the global water cycle. Bulletin of the American Meteorological Society , 96(7):1097--1115

    work page 2015

  21. [21]

    C., Hasselmann, K., Cubasch, U., Mitchell, J

    Hegerl, G. C., Hasselmann, K., Cubasch, U., Mitchell, J. F., Roeckner, E., Voss, R., and Waszkewitz, J. (1997). Multi-fingerprint detection and attribution analysis of greenhouse gas, greenhouse gas-plus-aerosol and solar forced climate change. Climate Dynamics , 13(9):613--634

    work page 1997

  22. [22]

    J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J

    Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., ...

    work page 2020

  23. [23]

    M., Winter, J

    Huang, H., Patricola, C. M., Winter, J. M., Osterberg, E. C., and Mankin, J. S. (2021). Rise in Northeast US extreme precipitation caused by Atlantic variability and climate change . Weather and Climate Extremes , 33:100351

    work page 2021

  24. [24]

    Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change

    IPCC (2023). Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change . Cambridge University Press

    work page 2023

  25. [25]

    S., Tett, S

    Jones, G. S., Tett, S. F., and Stott, P. A. (2003). Causes of atmospheric temperature change 1960--2000: A combined attribution analysis . Geophysical Research Letters , 30(5)

    work page 2003

  26. [26]

    Katzfuss, M., Hammerling, D., and Smith, R. L. (2017). A Bayesian hierarchical model for climate change detection and attribution . Geophysical Research Letters , 44(11):5720–5728

    work page 2017

  27. [27]

    and Hernández Ayala, J

    Keellings, D. and Hernández Ayala, J. J. (2019). Extreme Rainfall Associated With Hurricane Maria Over Puerto Rico and Its Connections to Climate Variability and Change . Geophysical Research Letters , 46(5):2964–2973

    work page 2019

  28. [28]

    V., Donat, M

    Kim, Y.-H., Min, S.-K., Zhang, X., Zwiers, F., Alexander, L. V., Donat, M. G., and Tung, Y.-S. (2015). Attribution of extreme temperature changes during 1951–2010. Climate Dynamics , 46(5–6):1769–1782

    work page 2015

  29. [29]

    Kirchmeier-Young, M. C. and Zhang, X. (2020). Human influence has intensified extreme precipitation in N orth A merica. Proceedings of the National Academy of Sciences , 117(24):13308--13313

    work page 2020

  30. [30]

    C., Zwiers, F

    Kirchmeier-Young, M. C., Zwiers, F. W., Gillett, N. P., and Cannon, A. J. (2017). Attributing extreme fire risk in Western Canada to human emissions . Climatic Change , 144:365--379

    work page 2017

  31. [31]

    and Hripcsak, G

    Kleinberg, S. and Hripcsak, G. (2011). A review of causal inference for biomedical informatics. Journal of Biomedical Informatics , 44(6):1102–1112

    work page 2011

  32. [32]

    Knutson, T. R. and Zeng, F. (2018). Model assessment of observed precipitation trends over land regions: Detectable human influences and possible low bias in model trends . Journal of Climate , 31(12):4617--4637

    work page 2018

  33. [33]

    Kunkel, K. E. (2003). North A merican trends in extreme precipitation. Natural Hazards , 29(2):291--305

    work page 2003

  34. [34]

    Lau, Y. T. A., Wang, T., Yan, J., and Zhang, X. (2023). Extreme value modeling with errors-in-variables in detection and attribution of changes in climate extremes. Statistics and Computing , 33(6)

    work page 2023

  35. [35]

    M., Hurtt, G

    Lawrence, D. M., Hurtt, G. C., Arneth, A., Brovkin, V., Calvin, K. V., Jones, A. D., Jones, C. D., Lawrence, P. J., Noblet-Ducoudr \'e , N. d., Pongratz, J., et al. (2016). The L and U se M odel I ntercomparison P roject ( LUMIP ) contribution to CMIP6 : rationale and experimental design. Geoscientific Model Development , 9(9):2973--2998

    work page 2016

  36. [36]

    J., Ahn, M.-S., Ordonez, A., Ullrich, P

    Lee, J., Gleckler, P. J., Ahn, M.-S., Ordonez, A., Ullrich, P. A., Sperber, K. R., Taylor, K. E., Planton, Y. Y., Guilyardi, E., Durack, P., Bonfils, C., Zelinka, M. D., Chao, L.-W., Dong, B., Doutriaux, C., Zhang, C., Vo, T., Boutte, J., Wehner, M. F., Pendergrass, A. G., Kim, D., Xue, Z., Wittenberg, A. T., and Krasting, J. (2024). Systematic and object...

    work page 2024

  37. [37]

    Lenssen, N. J. L., Schmidt, G. A., Hansen, J. E., Menne, M. J., Persin, A., Ruedy, R., and Zyss, D. (2019). Improvements in the GISTEMP Uncertainty Model . Journal of Geophysical Research: Atmospheres , 124(12):6307–6326

    work page 2019

  38. [38]

    Li, Y., Chen, K., Yan, J., and Zhang, X. (2021). Uncertainty in optimal fingerprinting is underestimated. Environmental Research Letters , 16(8):084043

    work page 2021

  39. [39]

    A., Lin, C., Nijssen, B., Mishra, V., Andreadis, K

    Livneh, B., Rosenberg, E. A., Lin, C., Nijssen, B., Mishra, V., Andreadis, K. M., Maurer, E. P., and Lettenmaier, D. P. (2014). A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States : Update and extensions. Journal of Climate , 27(1):478

    work page 2014

  40. [40]

    J., Durre, I., Vose, R

    Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., and Houston, T. G. (2012). An overview of the G lobal H istorical C limatology N etwork- D aily database. Journal of Atmospheric and Oceanic Technology , 29(7):897--910

    work page 2012

  41. [41]

    W., and Hegerl, G

    Min, S.-K., Zhang, X., Zwiers, F. W., and Hegerl, G. C. (2011). Human contribution to more-intense precipitation extremes. Nature , 470(7334):378

    work page 2011

  42. [42]

    M., Fuglestvedt, J., Gillett, N., Haustein, K., et al

    Mitchell, D., AchutaRao, K., Allen, M., Bethke, I., Beyerle, U., Ciavarella, A., Forster, P. M., Fuglestvedt, J., Gillett, N., Haustein, K., et al. (2017). Half a degree additional warming, prognosis and projected impacts (happi): background and experimental design. Geoscientific Model Development , 10(2):571--583

    work page 2017

  43. [43]

    P., Frumhoff, P., Bowery, A., Wallom, D., and Allen, M

    Mitchell, D., Heaviside, C., Vardoulakis, S., Huntingford, C., Masato, G., Guillod, B. P., Frumhoff, P., Bowery, A., Wallom, D., and Allen, M. (2016). Attributing human mortality during extreme heat waves to anthropogenic climate change. Environmental Research Letters , 11(7):074006

    work page 2016

  44. [44]

    R., Kim, K.-Y., Shen, S

    North, G. R., Kim, K.-Y., Shen, S. S. P., and Hardin, J. W. (1995). Detection of Forced Climate Signals. Part 1: Filter Theory . Journal of Climate , 8(3):401–408

    work page 1995

  45. [45]

    A., and Newman, R

    Noy, I., Wehner, M., Stone, D., Rosier, S., Frame, D., Lawal, K. A., and Newman, R. (2023). Event attribution is ready to inform loss and damage negotiations. Nature Climate Change , 13(12):1279–1281

    work page 2023

  46. [46]

    A., Medicine, et al

    of Sciences Engineering, N. A., Medicine, et al. (2016). Attribution of extreme weather events in the context of climate change . National Academies Press

    work page 2016

  47. [47]

    Ombadi, M., Nguyen, P., Sorooshian, S., and Hsu, K. (2020). Evaluation of methods for causal discovery in hydrometeorological systems. Water Resources Research , 56(7)

    work page 2020

  48. [48]

    Paciorek, C., Stone, D., and Wehner, M. (2018). Quantifying statistical uncertainty in the attribution of human influence on severe weather. Weather and Climate Extremes , 20:69--80

    work page 2018

  49. [49]

    M., Wehner, M

    Pall, P., Patricola, C. M., Wehner, M. F., Stone, D. A., Paciorek, C. J., and Collins, W. D. (2017). Diagnosing conditional anthropogenic contributions to heavy Colorado rainfall in September 2013 . Weather and Climate Extremes , 17:1–6

    work page 2017

  50. [50]

    and Wehner, M

    Patricola, C. and Wehner, M. (2018). Anthropogenic influences on major tropical cyclone events. Nature , 563(7731):339

    work page 2018

  51. [51]

    Pearl, J. (2009). Causality . Cambridge University Press

    work page 2009

  52. [52]

    J., Otto, F., Vautard, R., van der Wiel, K., King, A., Lott, F., Arrighi, J., Singh, R., and van Aalst, M

    Philip, S., Kew, S., van Oldenborgh, G. J., Otto, F., Vautard, R., van der Wiel, K., King, A., Lott, F., Arrighi, J., Singh, R., and van Aalst, M. (2020). A protocol for probabilistic extreme event attribution analyses. Advances in Statistical Climatology, Meteorology and Oceanography , 6(2):177--203

    work page 2020

  53. [53]

    W., Azaïs, J.-M., and Naveau, P

    Ribes, A., Zwiers, F. W., Azaïs, J.-M., and Naveau, P. (2017). A new statistical approach to climate change detection and attribution. Climate Dynamics , 48:367--386

    work page 2017

  54. [54]

    D., Collins, W

    Risser, M. D., Collins, W. D., Wehner, M. F., O’Brien, T. A., Huang, H., and Ullrich, P. A. (2024a). Anthropogenic aerosols mask increases in US rainfall by greenhouse gases . Nature Communications , 15(1)

    work page

  55. [55]

    D., Collins, W

    Risser, M. D., Collins, W. D., Wehner, M. F., O’Brien, T. A., Paciorek, C. J., O’Brien, J. P., Patricola, C. M., Huang, H., Ullrich, P. A., and Loring, B. (2022). A framework for detection and attribution of regional precipitation change: Application to the United States historical record . Climate Dynamics

    work page 2022

  56. [56]

    D., Paciorek, C

    Risser, M. D., Paciorek, C. J., O’Brien, T. A., Wehner, M. F., and Collins, W. D. (2019). Detected changes in precipitation extremes at their native scales derived from in situ measurements. Journal of Climate , 32(23):8087--8109

    work page 2019

  57. [57]

    D., Stone, D

    Risser, M. D., Stone, D. A., Paciorek, C. J., Wehner, M. F., and Angélil, O. (2017). Quantifying the effect of interannual ocean variability on the attribution of extreme climate events to human influence. Climate Dynamics , 49(9–10):3051–3073

    work page 2017

  58. [58]

    Risser, M. D. and Wehner, M. F. (2017). Attributable human-induced changes in the likelihood and magnitude of the observed extreme precipitation during Hurricane Harvey . Geophysical Research Letters , 44(24):12,457--12,464

    work page 2017

  59. [59]

    D., Wehner, M

    Risser, M. D., Wehner, M. F., O’Brien, J. P., Patricola, C. M., O’Brien, T. A., Collins, W. D., Paciorek, C. J., and Huang, H. (2021). Quantifying the influence of natural climate variability on in situ measurements of seasonal total and extreme daily precipitation. Climate Dynamics , 56:3205--–3230

    work page 2021

  60. [60]

    D., Zhang, L., and Wehner, M

    Risser, M. D., Zhang, L., and Wehner, M. F. (2024b). Impossible temperatures made possible with climate change. In preparation

    work page

  61. [61]

    Rothman, K. J. and Greenland, S. (2005). Causation and causal inference in epidemiology. American Journal of Public Health , 95(S1):S144–S150

    work page 2005

  62. [62]

    D., Wehner, M

    Santer, B. D., Wehner, M. F., Wigley, T., Sausen, R., Meehl, G., Taylor, K., Ammann, C., Arblaster, J., Washington, W., Boyle, J., et al. (2003). Contributions of anthropogenic and natural forcing to recent tropopause height changes. Science , 301(5632):479--483

    work page 2003

  63. [63]

    B., Stott, P

    Sarojini, B. B., Stott, P. A., and Black, E. (2016). Detection and attribution of human influence on regional precipitation. Nature Climate Change , 6(7):669--675

    work page 2016

  64. [64]

    E., McCormick, M

    Sato, M., Hansen, J. E., McCormick, M. P., and Pollack, J. B. (1993). Stratospheric aerosol optical depths, 1850--1990. Journal of Geophysical Research: Atmospheres , 98(D12):22987--22994

    work page 1993

  65. [65]

    J., Ghan, S., Gregory, J

    Schmidt, A., Mills, M. J., Ghan, S., Gregory, J. M., Allan, R. P., Andrews, T., Bardeen, C. G., Conley, A., Forster, P. M., Gettelman, A., et al. (2018). Volcanic radiative forcing from 1979 to 2015. Journal of Geophysical Research: Atmospheres , 123(22):12--491

    work page 2018

  66. [66]

    Schreiber, T. (2000). Measuring information transfer. Physical Review Letters , 85(2):461–464

    work page 2000

  67. [67]

    Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal , 27(3):379–423

    work page 1948

  68. [68]

    Climate Change 2022 - Mitigation of Climate Change: Working Group III Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change

    Shukla, P., Skea, J., Slade, R., and co authors (2023). Climate Change 2022 - Mitigation of Climate Change: Working Group III Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change . Cambridge University Press

    work page 2023

  69. [69]

    T., Noy, I., Wehner, M

    Smiley, K. T., Noy, I., Wehner, M. F., Frame, D., Sampson, C. C., and Wing, O. E. J. (2022). Social inequalities in climate change-attributed impacts of Hurricane Harvey . Nature Communications , 13(1)

    work page 2022

  70. [70]

    Smith, C. (2021). chrisroadmap/aerosol-history: Energy budget constraints on the time history of aerosol forcing. Zenodo

    work page 2021

  71. [71]

    J., Harris, G

    Smith, C. J., Harris, G. R., Palmer, M. D., Bellouin, N., Collins, W., Myhre, G., Schulz, M., Golaz, J.-C., Ringer, M., Storelvmo, T., and Forster, P. M. (2021). Energy budget constraints on the time history of aerosol forcing and climate sensitivity. Journal of Geophysical Research: Atmospheres , 126(13)

    work page 2021

  72. [72]

    M., Gillett, N

    Smith, D. M., Gillett, N. P., Simpson, I. R., Athanasiadis, P. J., Baehr, J., Bethke, I., Bilge, T. A., Bonnet, R., Boucher, O., Findell, K. L., Gastineau, G., Gualdi, S., Hermanson, L., Leung, L. R., Mignot, J., Müller, W. A., Osprey, S., Otterå, O. H., Persad, G. G., Scaife, A. A., Schmidt, G. A., Shiogama, H., Sutton, R. T., Swingedouw, D., Yang, S., Z...

    work page 2022

  73. [73]

    Sobel, M. E. (2000). Causal inference in the social sciences. Journal of the American Statistical Association , 95(450):647–651

    work page 2000

  74. [74]

    A., Christidis, N., Folland, C., Perkins-Kirkpatrick, S., Perlwitz, J., Shiogama, H., Wehner, M

    Stone, D. A., Christidis, N., Folland, C., Perkins-Kirkpatrick, S., Perlwitz, J., Shiogama, H., Wehner, M. F., Wolski, P., Cholia, S., Krishnan, H., Murray, D., Angélil, O., Beyerle, U., Ciavarella, A., Dittus, A., Quan, X.-W., and Tadross, M. (2019). Experiment design of the International CLIVAR C20C+ Detection and Attribution project . Weather and Clima...

    work page 2019

  75. [75]

    Sugihara, G., May, R., Ye, H., Hsieh, C.-h., Deyle, E., Fogarty, M., and Munch, S. (2012). Detecting causality in complex ecosystems. Science , 338(6106):496–500

    work page 2012

  76. [76]

    F., Stott, P

    Tett, S. F., Stott, P. A., Allen, M. R., Ingram, W. J., and Mitchell, J. F. (1999). Causes of twentieth-century temperature change near the Earth's surface . Nature , 399(6736):569--572

    work page 1999

  77. [77]

    H., Scheffer, M., Brovkin, V., Lenton, T

    van Nes, E. H., Scheffer, M., Brovkin, V., Lenton, T. M., Ye, H., Deyle, E., and Sugihara, G. (2015). Causal feedbacks in climate change. Nature Climate Change , 5(5):445–448

    work page 2015

  78. [78]

    J., Philip, S., Kew, S., Vautard, R., Boucher, O., Otto, F., Haustein, K., Soubeyroux, J.-M., Ribes, A., Robin, Y., et al

    Van Oldenborgh, G. J., Philip, S., Kew, S., Vautard, R., Boucher, O., Otto, F., Haustein, K., Soubeyroux, J.-M., Ribes, A., Robin, Y., et al. (2019). Human contribution to the record-breaking June 2019 heat wave in France . World Weather Attribution

    work page 2019

  79. [79]

    and Sampson, C

    Wehner, M. and Sampson, C. (2021). Attributable human-induced changes in the magnitude of flooding in the Houston, Texas region during Hurricane Harvey . Climatic Change , 166(1–2)

    work page 2021

  80. [80]

    D., Wehner, M

    Zhang, L., Risser, M. D., Wehner, M. F., and O’Brien, T. A. (2024). Leveraging Extremal Dependence to Better Characterize the 2021 Pacific Northwest Heatwave . Journal of Agricultural, Biological and Environmental Statistics

    work page 2024

Showing first 80 references.