THIS POST IS A CRITICAL EVALUATION OF THE DIFFENBAUGH, SINGH, AND SWAIN 2017 PAPER [LINK]  THAT IS OFTEN CITED AS THE SCIENTIFIC BASIS FOR THE ATTRIBUTION OF EXTREME WEATHER EVENTS TO AGW CLIMATE CHANGE.

CITATION: Quantifying the influence of global warming on unprecedented extreme climate events. Diffenbaugh, Singh, & Swain ET AL, PNAS April 24, 2017, Edited by Kerry A. Emanuel. Received for review October 31, 2016. 

PART-1: WHAT THE PAPER SAYS 

1. ABSTRACT: Efforts to understand the influence of historical global warming on individual extreme climate events have increased over the past decade. However, despite substantial progress, events that are unprecedented in the local observational record remain a persistent challenge. Leveraging observations and a large climate model ensemble, we quantify uncertainty in the influence of global warming on the severity and probability of the historically hottest month, hottest day, driest year, and wettest 5-d period for different areas of the globe. We find that historical warming has increased the severity and probability of the hottest month and hottest day of the year at >80% of the available observational area. Our framework also suggests that the historical climate forcing has increased the probability of the driest year and wettest 5-d period at 57% and 41% of the observed area, respectively, although we note important caveats. For the most protracted hot and dry events, the strongest and most widespread contributions of anthropogenic climate forcing occur in the tropics, including increases in probability of at least a factor of 4 for the hottest month and at least a factor of 2 for the driest year. We also demonstrate the ability of our framework to systematically evaluate the role of dynamic and thermodynamic factors such as atmospheric circulation patterns and atmospheric water vapor, and find extremely high statistical confidence that anthropogenic forcing increased the probability of record-low Arctic sea ice extent. 
2. SUMMARY OF EVENT ATTRIBUTION ANALYSIS: DATA AND RESULTS:
3. THE UNCERTAINTY PROBLEM:  These data contain large sampling errors because we have data for only a few decades and that leaves us with large uncertainties in the estimation of event probability. How does event attribution analysis get around this uncertainty problem? Here the authors of {Diffenbaugh Singh & Swain 2017} write that they were able to overcome their uncertainty problem with the methodology  used in {Singh & Diffenbaugh 2013} and  {Swain & Diffenbaugh 2014}. But as we see in the citations, they are actually citing themselves.
4. {Singh & Diffenbaugh 2013} studied severe precipitation in Northern India in June 2013 and found “Our statistical analysis, combined with our diagnosis of the atmospheric environment, demonstrates that the extreme June 2013 total precipitation in northern India was at a century-scale event. Precise quantification of the likelihood of the event in the current and preindustrial climates is limited by the relatively short observational record.
5. {Swain & Diffenbaugh 2014} studied the 2013-2014 drought in California and found “The 2013/14 California drought was an exceptional climate event. A highly persistent large-scale meteorological pattern over the northeastern Pacific led to observationally unprecedented geopotential height and precipitation anomalies over a broad region. The very strong ridging and highly amplified meridional flow near the West Coast of North America in 2013/14 was structurally similar to but spatially and temporally more extensive than atmospheric configurations that have been previously linked to extreme dryness in California. We find that extreme geopotential height values (i.e. warmer air} in this region occur more frequently in the present climate than in the absence of human emissions {Note-1:  Geopotential height represents the height of the pressure surface. Cold air is more dense than warm air and that causes pressure surfaces to be lower in colder air masses and higher in warmer air masses}.   {Note-2: “the absence of human emissions” is a reference to the pre-industrial. The choice of language emphasizes a climate science bias and not evidence of fossil fuel emissions as the cause of the drought}. The human and environmental impacts of the 2013/14 California drought were amplified by the timing of the event. The event began in January 2013, abruptly truncating what had initially appeared to be a wet rainy season following very heavy precipitation during November–December 2012. By persisting through January 2014, the event also effectively delayed the start of the subsequent rainy season by at least four months. The rapid onset and persistent high intensity of drought conditions presented unique challenges for decision makers tasked with making choices about the allocation of water to urban, agricultural, and environmental interests. Together, the complexity and severity of the observed drought impacts, coupled with our finding that global warming has increased the probability of extreme North Pacific geopotential heights similar to those associated with the 2013/14 drought, suggest that understanding the link between climate change and persistent North Pacific ridging events will be crucial in characterizing the future risk of severe drought in California.
6. The authors cite these two of their earlier works as references that validate their methodology described as “we evaluate the climate model’s simulation of interannual variability in each climate indicator. Previous event attribution studies have made this evaluation using the Kolmogorov−Smirnov test (22, 34, 38). However, we find that the Anderson−Darling test, which gives more weight to the tails of the distribution, produces a more restrictive comparison with observations for the four extreme climate variables. We first correct the mean of the Pre-Industrial Control Simulation to be equal to the mean of the detrended observations. We then use the A-D test to quantify the agreement between the mean-corrected Pre-Industrial Simulation and the detrended observations. We reject the climate model if the A-D test yields a P value less than 0.05, as this suggests that the model output does not come from the same statistical population as the observations.
7. The authors conclude as follows:  CONCLUSION:  We apply four event attribution metrics to a suite of climate variables, including globally gridded temperature and precipitation observations. Our framework is designed to proceed if there is statistical confidence in the fit between the parametric distribution and the observations, if the parametric fit produces a finite solution across the uncertainty distribution, and if the climate model is able to accurately simulate the observed distribution of the variable. Our systematic analysis of global temperature and precipitation data show that these criteria are often met, but also that large areas of the globe can violate these criteria. The failure of events to meet these criteria arises from at least three conditions. First, unprecedented events result from a complex combination of interacting physical causes. Second, given the rarity of the event, the limitations of the observed record, and the nonstationarity of the climate system, quantifying the probability in the current climate can be highly uncertain. Third, given the complexity of the physical causes, climate models may not accurately simulate the underlying physical processes, or their probability of occurrence. Our results therefore highlight at least five important priorities for “single event attribution”: They are as follows:  (1) understanding the contributions of different physical causes to a particular event.  (2) using formal hypothesis testing to quantify the uncertainty in the probability of both the event and the contributing physical causes, (3) ensuring accurate assessment of the fidelity of the statistical and physical models to the observational data, (4)  distinguishing changes in the probability of extremes from changes in the mean, and (5)  systematically differentiating “absence of evidence” of a causal link from “evidence of absence.

CRITICAL COMMENTARY

1. Event Attribution Science, (if science it it is), is a methodology of using climate model simulations to attribute extreme weather events such as heat waves, floods, and droughts, post hoc, (after the fact) to anthropogenic global warming thought to be driven by fossil fuel emissions and thereby ultimately to the use of fossil fuels. The procedure suffers from several weaknesses including confirmation bias, circular reasoning, and the extreme localization in time and space in the interpretation of a theory about long term trends in global mean temperature. The localization issue is described in the literature as “internal variability” of climate.
2. Internal Climate Variability: The localization issue refers to the impossibility of separating the natural from the anthropogenic in what is described as internal climate variability. This observation derives from the finding that although climate models can relate long term global trends to fossil fueled anthropogenic global warming, this relationship falls apart at brief time scales of 30 years or less and with localization of climate to geographical regions less than large latitudinal extents. Global warming theory is a global issue and its interpretation is not possible in specific regions, particularly when the region is selected post hoc.
3. In a related post on Internal Variability [LINK] , we find that “at short time scales of 30 years or less, or in limited geographical extents,  internal variability of climate confounds assessment of human-induced climate change and imposes irreducible limits on the accuracy of climate change projections and climate change impacts“. 
4. The Dark Bureaucratic Origins of Event Attribution [LINK] :  Event Attribution Analysis is best understood in the context of its origins. A necessary and assumed catastrophic nature of AGW is needed as the rationale for the UNFCCC policy that requires Annex I countries to reduce emissions by changing their energy infrastructure from fossil fuels to renewables. This line of reasoning is weakened by an inability of climate science to produce empirical evidence that relates extreme weather disasters to emissions. Of particular note in this regard is that claims made by the IPCC in 2007 with regard to the effect of AGW on the frequency and intensity of tropical cyclones, droughts, and floods were retracted in their next Assessment Report in 2014. Thus, climate scientists, though convinced of the causal connection between AGW and extreme weather events, are nevertheless unable to provide acceptable empirical evidence to support what to them is obvious and “unequivocal” but for which climate science has neither empirical evidence nor a methodology that could serve as the tool for presenting such evidence. 
5. A breakthrough came for climate science in 2013 when the Warsaw International Mechanism (WIM) was signed [LINK] . This mechanism has to do with the complex classification of nation states in the Kyoto Protocol and the UNFCCC in which poor developing nations of the Global South are classified as (Non-Annex countries) with no climate action obligations. Rich developed Western countries of the Global North (Annex-1 countries) are assigned the entire burden of global emission reduction along with the additional burden of providing financial compensation to the non-Annex countries of the Global South for extreme weather impacts of climate change. When the Annex-1 providers of climate impact compensation funds requested evidence to separate extreme weather events that are natural from those caused AGW climate change, the United Nations organized the meeting in Warsaw in 2013 to discuss and resolve this issue.
6. The Warsaw International Mechanism (WIM) of 2013 has redefined climate change adaptation funding as a form of compensation for “loss and damage” suffered by nonAnnex countries because of sea level rise or extreme weather events caused by fossil fuel emissions which are thought to be mostly a product of Annex-1 countries. Accordingly, the WIM requires that loss and damage suffered by the nonAnnex countries for which compensation is sought from climate adaptation funds must be attributable to fossil fuel emissions.
7. A probabilistic methodology was devised to address the need for attribution in the WIM and It has gained widespread acceptance in both technical and policy circles as a tool for the allocation of limited climate adaptation funds among competing needs of the non-Annex countries. The probabilistic event attribution methodology (PEA) uses a large number of climate model experiments with multiple models and a multiplicity of initial conditions. A large sample size is used because extreme weather events are rare and their probability small by definition. The probability of an observed extreme weather event with anthropogenic emissions and the probability without anthropogenic emissions are derived from climate model experiments as P1 and P0.
8. If the probability with emissions (P1) exceeds the probability without emissions (P0), the results are interpreted to indicate that emissions played a role in the occurrence of the event in question and that therefore it is fundable. Otherwise the event is assumed to be a product of natural variation alone. The probability that fossil fuel emissions played a role in the extreme weather event is represented as P = (P1-P0)/P0. The procedure serves the bureaucratic needs of the UN but is mired in procedural issues such as confirmation bias and uncertainty.
9. A contentious issue in PEA analysis is that of uncertainty in the values of P0 and P1 and in the model results themselves. Policy analysts fear that the large uncertainties of climate models provide sufficient reason to question the reliability of PEA to serve its intended function as a criterion for access to climate adaptation funds. Mike Hulme and others argue that much greater statistical confidence in the PEA test is needed to justify denial of adaptation funding for loss and damage from weather extremes that do not pass the PEA test.
10. The greater concern is that climate science assumes the relationship between AGW and extreme weather impacts but suffers from a critical need for a methodology to provide evidence for it. It is in this context that climate science seized upon the bureaucratic PEA procedure of the WIM,  extended the interpretation of PEA results beyond their intended function of fund allocation, renamed it as Event Attribution Science, and adopted it as the climate science methodology that can relate extreme weather events to Anthropogenic Global Warming (AGW).
11. This  enthusiastic innovation in climate science was initiated by climate scientist Friederike Otto (Oxford). She is shown in the third photograph at the top of this page. The first two photographs are of Noah Diffenbaugh (Stanford), the lead author of the Event Attribution paper presented here and co-author Kerry Emanuel (MIT), a leading figure in the attribution of rising hurricane intensity and destructiveness to AGW climate change described in a related post [LINK] .
12. For the purpose of this extension of the PEA procedure of the WIM to a form of climate science, its name was changed from PEA to Event Attribution Analysis and then elevated by Scientific American to Event Attribution Science in an article extolling its virtues.  In a related post [LINK] , it is shown that the methodology suffers from confirmation bias and the so called Texas Sharpshooter fallacy. The selection of the event after the fact provides a selection bias and as we see in the three papers discussed above, if the statistics are not initially satisfactory, the data can be tortured until something is retrieved that rationalizes the attribution. The language of the interpretation of results implies a direct attribution to fossil fuel emissions instead of to temperature. No effort is made to compare the event to recent post AGW events at similar and lower temperatures to establish the relationship between temperature and the severity of the weather event. Also no data or rationale is provided for events at the same or later time that may have been of a lesser intensity.
13. Since global warming is a theory about long term trends in global mean temperature and events are by definition localized in time and space, the event attribution should include a a comparative analysis with regions that have warmed at different rates to support their attribution. In general, the explanation of time and space constrained events with a theory about a long term warming n global mean temperature must include a causal connection between these very different phenomena in terms of time and space.
14. Recent research in Internal Climate Variability provides further insight into this weakness of event attribution methodology in terms of an impossibility of relating localized climate events constrained by time and space to the long term warming trend in global mean temperature that has been attributed to fossil fuel emissions. This research is described in a related post on this site [LINK] . The essence of the internal variability issue is that AGW climate science is a system of making long range forecasts for global mean temperature and its extension to shorter time spans or regional climate is not possible because shorter time spans and regional climate are driven mostly by internal climate variability that is beyond AGW climate science. As stated by the authors of these papers, “at short time scales of 30 years or less, or in limited geographical regions not described as large latitudinal sections of the globe, internal variability in the climate system confounds assessment of human-induced climate change and imposes irreducible limits on the accuracy of climate change projections and climate change impacts. The internal variability finding limits the ability of climate science to attribute localized extreme weather events to anthropogenic global warming.
15. The finding of the DIFFENBAUGH, SINGH, AND SWAIN 2017 PAPER presented above is that : historical warming has increased the severity and probability of the hottest month and hottest day of the year at >80% of the available observational area. Our framework also suggests that the historical climate forcing has increased the probability of the driest year and wettest 5-d period at 57% and 41% with the caveat that for the most protracted hot and dry events, the strongest and most widespread contributions of anthropogenic climate forcing occur in the tropics, including increases in probability of at least a factor of 4 for the hottest month and at least a factor of 2 for the driest year.
16. In the finding both the time span to be studied and the percent of the observed area are different but these differences are not explained or considered in there interpretation. IN CONCLUSION, WE FIND THE ATTRIBUTION OF EXTREME WEATHER EVENTS TO AGW GLOBAL WARMING IS NOT CONSTRAINED IN ANY WAY SO THAT WHATEVER DIFFERENCES CAN BE FOUND ARE ARBITRARILY ATTRIBUTED TO ANTHROPOGENIC GLOBAL WARMING. THE TIME SCALE FOR THE STUDY AND THE EXTENT OF THE OBSERVED AREA AFFECTED ARE NOT PRE-SPECIFIED BUT REMAIN FLUID CONSTRAINED ONLY BY THE CONFIRMATION BIAS OF THE RESEARCHER.
17. HERE WE FIND THAT EVENT ATTRIBUTION ANALYSIS TO DETERMINE THE IMPACT OF WARMING ON EXTREME WEATHER EVENTS IS NOT CREDIBLE BECAUSE THERE ARE NO CONSTRAINTS IN THE METHODOLOGY OR IN THE INTERPRETATION OF THE DATA SUCH THAT THE CONFIRMATION BIAS OF THE RESEARCHER GUIDES THE SELECTION OF THE DATA AND THEIR INTERPRETATION. 
18. THE METHODOLOGY BOILS DOWN TO THIS: 1. FIND AN EXTREME WEATHER EVENT SOMEWHERE. 2. FIND A WAY TO RELATE IT TO AGW. UNBIASED OBJECTIVE SCIENTIFIC INQUIRY BEGINS WITH THE RESEARCH QUESTION; BUT CONFIRMATION BIASED EVENT ATTRIBUTION RESEARCH BEGINS WITH THE DATA WITH THE INTERPRETATION OF THE DATA GUIDED BY CONFIRMATION BIAS.
19. IN A RELATED WORK WE SHOW A SIMILAR CONFIRMATION BIAS / CIRCULAR REASONING  IN A STUDY by KERRY EMANUEL ABOUT THE EFFECT OF GLOBAL WARMING ON THE “DESTRUCTIVENESS OF HURRICANES”: [LINK] , A FURTHER DEMONSTRATION OF CIRCULAR REASONING AND CONFIRMATION BIAS IN CLIMATE SCIENCE.  MORE ON HURRICANES [LINK] .