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We are keen to receive review comments for our new paper which is now available for open peer review (pdf).

Julian Morris: Weathering the Extremes

This report offers an overview of the likely effects that climate change has had on the incidence of extreme weather events, ranging from storms and floods to droughts and wildfires, and the extent to which those events have caused harm. In so doing, it addresses many (but not all) of the ways by which climate change might cause suffering and death. To do so, it describes the historical evidence for changes in the frequency and severity of extreme weather events, the mortality associated with such events, and their economic impact. It also discusses ways that humans have adapted to extreme weather events and strategies that will make humanity more resilient in the future.

Submitted comments and contributions will be subject to a moderation process and will be published, provided they are substantive and not abusive.

Review comments should be emailed to:

The deadline for review comments is 31 March 2024.


Colin Barton, Melbourne, Australia

A very impressive piece of work – well done.

I have one major suggestion that I think would enhance your results at the cost of a little more effort.

Having one mean line on many of the charts with the actual data does show it all, but the relevance of the often large individual divergences from the mean is not brought out.

Showing the standard deviations from the mean as a band labelled, not as 1 or 2 or even 3 standard deviations about the mean,  but as either 66% , 95%, or 99% confidence levels, would immediately show the reader that in the selected charts, the slight change in the slope of the mean line in either direction is statistically irrelevant.

The proviso of course is that, in accordance with usual statistical methods (and plain common sense) it is based on the normal Gaussian distribution.

Looking at the span of data on most of the charts this seems valid and if one needs confirmation, plot the data as a Gaussian curve to confirm it by sight or do a correlation coefficient r on it.

Extreme limits showing a little skewness are to be expected (nothing is perfect in our World) and would likely be inconsequential.


Dr Peter Alberry  

I read “Weathering the Extremes” with great interest as I have been collecting “raw data” on climatic events for some time.

The data in this paper seem to be broadly consistent with numerous other studies.

My only comment of significance is that a short explanation of the meaning of the correlation coefficients “r” and “r2” might be helpful.  

I was taught that an r value of 0.5 (r2 = 0.25) was an indication of non-signficance or randomness in terms of the data correlation being examined and that correlations of greater than 0.9 (R2 =0.8) were really required for any scientifically reliable measure.

I’m aware that the social sciences can infer meaningful correlations from r values as low as 0.5 but I believe that Climate Change papers usually purport to be well within the realm of “science”.


Professor Ross McKitrick

I’ve read Julian Morris’s paper. I think it is very well done. He does a good job of surveying the physical measurements then relating them to economic and social measurements. His use of the underlying original data sources is quite good.

One paper that might be worth mentioning in the context of adaptation to temperature changes is 

  • J.M. Doremus, I. Jacqz and S. Johnston (2022), “Sweating the energy bill: Extreme weather, poor households, and the energy spending gap.” Journal of Environmental Economics and Management, doi:

They show that wealthy and poor households adjust their energy expenditures at similar rates in response to moderate temperature swings, but not in response to extreme temperature swings. When temperatures swing to very cold levels (< 5°C) energy spending in high-income households rises by 1.2 percent but in low-income households by only 0.5 percent. On very hot days (>30°C) electricity spending in high-income households rises by 0.5 percent but does not change at all in low-income households. The latter result is observed even in subsamples where all households have AC. The implication is that even with widespread adoption of home heating and cooling systems, inability to afford energy leaves low-income households exposed to weather extremes. 


Ralph Alexander

This is a good paper, complementary to my own GWPF reports on weather extremes. A few comments:

  1. Just before Figure 1, Morris states ‘Since geophysical disasters clearly are not caused by climate change, it seems likely that much of the documented increase in both is a result of other factors.’ I think a short discussion of possible other factors would be appropriate here. Both Paul Homewood and I have tied the increase in extreme weather reporting since 1998 to the advent of the Internet. But it appears from Figure 1 that the increase began earlier – quite likely due to the beginning of satellite measurements in the 1960s.
  2. In Section 5.2 on Mortality from Drought and the Discussion in Section 5.4, the author may want to mention that several major famines occurred as a result of droughts in the early 20th century. This would amplify his references to later ‘improved food production and distribution.’
  3. In the Discussion of wildfires in Section 6.4, ‘inadequate forest/fire management’ needs amplification.


Dr John Carr

The report presents data relating to extreme weather which is subject to almost daily use in the media for climate alarmist propaganda. Unfortunately features of the raw data on extreme weather mean many plots need careful interpretations to avoid perplexity. The problem is that data numbers related to extreme weather events over the past century often change with time for multiple reasons in data reporting which are unrelated to climate change. These reasons range from technology of data collection to population movements into regions more susceptible to damage. In my opinion, many of the plots shown in this report are open to misinterpretation.

Figures 8.1 and 8.2 are examples of the sorts of plots which can easily be misinterpreted. These two plots show a summary of the incidence of classes of extreme weather events indicating a large increase from 1970 to 2000 which a casual might conclude is due to global warming. The accompanying text explains that the increase is due to the incidence of floods and may have a bias in the EM-DAT data. The text does not suggest how the reader can take this bias into account and make any sensible conclusion from these two plots.

The vital point of bias in the EM-DAT data is already addressed in section 1 with figure 1, which compares extreme weather events with geophysical disasters. The conclusion in the text is “it is likely that much of the documented increase is a result of other factors”, but without explaining what these factors are. This introductory section could treat the bias issue in much more detail than this rather odd plot.

The mortality plots in the report are often dominated by rare events which have caused significant numbers of deaths and are often not analysed in a completely objective way. Figures 2.7 and 2.8 are dominated by a small number of events. The text well describes the situation to arrive at the conclusion and in my opinion is adequate to get the point across. The figure 2.8 with a trend line and correlation coefficient is completely dominated by the 1900 event and does not help the explanation in my opinion. Figure 2.9 has a similar problem being dominated by the 1971 event. Again the text is adequate and the trend line in the plot is meaningless. Although damage not mortality, figure 2.11 is again dominated by a few events and the trend line is irrelevant. Plots in the sections 3 and 4 have similar issues.

The section 5 on drought has many complicated plots which do not seem necessary to support the conclusions. Figure 5.1 shows that the drought index averaged over the US fluctuates significantly. The trend line plotted shows an increase but in no way describes the data and so is misleading. Figures 5.2 and 5.3 show that drought are very regionally dependent so that average over continents must give meaningless results. Figure 5.3 show three different analyses of the same data indicating the complexities of the analysis but it is not clear why presenting this expert analysis is important in this report. Figure 5.5 indicates a big increase in damage after 1960 which is described as “highly likely … an artefact of the criteria for reporting”. Why is this important to present?

Section 6 on wildfires has a large number of plots, some of which exhibit the rare events feature. Again, I think the conclusions are not enhanced by such details.

Section 7 on extreme temperatures has a number of problems. The first is the lack of a definition of what is an extreme temperature. The first paragraph says “….there is no objective definition”, but all the plots use a definition and so that must be given, otherwise the plots are totally meaningless. Figure 7.1 has the issue of being dominated by enormous fluctuations but is summarized by a trend line which does not describe the bulk of the data. Here, the data point clearly diverge after 1990 but not clearly before. Figure 7.2 has the problem that the caption is missing, there are 8 plots labelled a) to h) but the definition of what the differences are is lacking. Figures 7.3 and 7.4 have the problem of a conclusion being drawn from a few dominant events. The paragraph after figure 7.5, which shows a large increase in deaths form extreme heat after 2000, says: “ However, this focus on extreme temperatures may give a misleading impression regarding the overall effects of changes in temperature on human mortality over the past few decades.” This reader is completely confused about what the message is in this section.

This report by Julian Morris has the same subject matter as that of Ralph Alexander published in 2022. The Alexander report focuses on the reporting of the IPCC AR6 on Extreme Weather. The style of the Alexander report makes it much easier to read than the report by Morris. It would seem natural in the context that the recent report should refer to the earlier report and make clear what is different. In the open debate on this report Ralph Alexander says the new one is “complementary” to his own but does not say how. 

The major difference that I perceive, is that the Morris report introduces mortality and in so doing addresses the claim from Greta Thunberg quoted at the beginning of the report saying: “…. People are suffering. People are dying….”. The report concludes: “The somewhat surprising answer is that although some people might be suffering and dying in part as a result of climate change, far fewer people are suffering and dying today of the kinds of problems most associated with climate change than were dying in decades past.” This seems to be the bottom line from the rather long and confusing report.


Professor Michael Alder

The paper is well researched and referenced. As this is not within my particular academic expertise my comments are limited.

I note that whilst the paper considers global issues, the concentration of data is mainly US and to a lesser extent UK limited. I also note conclusions in the section relate to mortality which in relation to the initial statement from Greta T. is acceptable, but a lot of the weather factors relate to famine and starvation, and this gets little coverage.

I note towards the end of the paper there is quite an optimistic section on agriculture and food production. I find this unbalanced. There is plenty of printed data from the UN and various universities, e.g. Minnesota, that indicates global warming reflected in weather conditions is severely affecting global food production. 

The role of soils in carbon capture and storage is very relevant in relation to sustainable land management and this deserves a mention in the paper.


Anthony Janio

It is good to see that scientists are finally starting to question the ‘97% of scientists believe’ narrative, by starting to challenge ‘received wisdom’.

As a former scientist and politician, I would like to say that the language in the report tends towards being written for scientists. The paper should take care to speak the language of politicians. Don’t be too fancy and too smug – it doesn’t help.

But my most important comment, is that science research is performed largely at the micro level in order to inform theories and models at the macro level. This is difficult to achieve. With both ‘Climate’ and ‘Covid-19’ public speakers make (made) ‘assumptions’ at the micro level in order to ‘inform’ at the macro level. This has broken the Scientific Method: without fundamental, low variable, relationships being ‘certain’ at the micro level, the higher theories/models don’t hold any serious validity (e.g. Covid deaths, man-made climate warming etc). This needs to be stressed to non-scientists, who have an unusual view of ‘science’.


Donal O’Callaghan

This paper takes a statement made by a political activist, which makes inferences about an anthropogenic impact on climate and because the statement has gained a great deal of traction. The paper is described as a “primer” and challenges the factual validity of the statement by examining evidence for changes in extreme weather events and discussing adaptation strategies.

As I read the paper a few questions hovered in my mind:

The term “climate change” is used widely in the paper, often as a “cause” of some phenomenon. Two issues arise here that need clarification.     

1 Climate change has more than one meaning, e.g. the definition of the U.N. which is anthropogenic climate change and the more general sense of changing climate due to any cause. I think the use in this monograph is mainly the latter but it would help if the usage were clarified.   

2 Treating climate change as a cause of extreme weather events is something of a tautology, as extreme weather events may be inherent to climate and/or to climate change. So I would suggest that the use of language could be clearer. It is reasonable to ask: are extreme weather events becoming more common, and if so, is there a known anthropogenic cause? Is humanity capable of adapting to extreme weather events?

Additional points:

Sec 4.1 begins “(Although) precipitation has increased both globally and in the U.S. over the past century….” I see no evidence cited for this statement.

Fig 5.5 Can you clarify if the damages cost in $ has been corrected for inflation?

Sec 5.4. I agree entirely that famines are related to wars (and also governments impeding the transport of food to where it is needed).

Fig 6.3 usefully shows a vertical red line indicating “policy & reporting change”. This needs to be clearly referenced.

Sec 6.2. The point about significantly increased housing in “wildland-urban interface” is very important and useful. This is correctly stated in the summing up in sec  6.4.

Sec 7 opens with “Humans are not well adapted to extreme temperatures.” I feel that this statement could be more nuanced, as for example those living near the artic circle and those in equatorial areas seem to adapt quite well (in my opinion). “As such, there is no objective definition [of extreme temperature]” – there probably is in medical usage. e.g. Sobolewski, A., Młynarczyk, M., Konarska, M., & Bugajska, J. (2021). The influence of air humidity on human heat stress in a hot environment. International Journal of Occupational Safety and Ergonomics, 27(1), 226–236 which refers to both temperature and humidity.

Chapter 7 is very well handled, including 7.4 Discussion.

Chapters 8 and 9 are very well written and sum up well.

Sec 9.2 p61. “As Figure 9.3 shows, countries whose citizens are less reliant on agriculture tend to score higher on the UNDP’s Human Development Index”. I presume that “less reliant on agriculture” is correlated with wealth creation through economic development facilitated by exploitation of affordable energy. The following sentence possibly says this in another way.