Climate and Weather Extremes

We evaluate climate using statistical summaries of weather data collected over longer time periods, including means, percentiles, and extremes of various weather parameters such as temperature, precipitation, and wind.  Climate can be evaluated on spatial scales ranging from global to regional to local and even micro-scale and for temporal scales ranging from days to months to seasons to years to decades to centuries to millennia and beyond.  Weather extremes are part of climate and both weather and climate vary depending on temporal and spatial scales.  Weather extremes and climate can also change over time and with longer time periods the magnitude of variation is likely to be larger.  This complexity makes evaluating climate trends and associated extreme weather event trends very difficult.  Any evaluation is relative – both spatially and temporally.

However, some simple statistics can be applied to examining weather extremes.  If we choose a local spatial scale and a millennia time scale, we can look at probabilities of the occurrence of extreme weather events, such as extreme cold and hot temperatures, extreme precipitation, and extreme drought for instance.  If we assume extreme weather events are random and select 1,000 weather monitoring locations somewhat evenly distributed around the globe and at least 200 kilometers apart, we can expect that on average each year one location will have a once in 1,000 year extreme weather event (relative to that location), somewhere around the globe.  Because of random effects, some years might have none and some might have two or three stations with once in 1,000 year events.  Similarly, we can expect that on average each year two locations will have a once in 500 year event, five locations will have a once in 200 year event, ten locations will have a once in 100 year event, 20 locations will have a once in 50 year event, and 50 locations will have a once in 20 year event (for statistics relative to each location).  These statistics apply to each type of extreme weather event separately, including extreme cold temperatures, extreme hot temperatures, lengthy heat waves, lengthy cold waves, extreme precipitation amounts, lengthy wet periods, and lengthy droughts.

The point is that with news media today reporting extreme weather events around the globe, there will be many very extreme weather events reported somewhere around the globe every year.  This situation is to be expected as a part of normal weather and climate conditions and is nothing unusual.

To evaluate whether there are any significant trends over time for extreme events is difficult and requires long periods of weather data.  From a global perspective, the necessary weather data records are insufficient spatially and temporally to determine any significant trends at present.  From an individual location perspective, there are a few locations with long periods of record where it may be possible to evaluate changes over time by comparing non-overlapping 30-year periods.  Ideally at least 150 years of complete data would be needed from a single location to have five different 30-year periods to compare for trend analysis, but very few locations have enough complete weather data for that length of time.  Consequently, we cannot have much confidence in any claimed trends for extreme weather events until weather data are accumulated over much longer time spans, ideally 200 to 300 years or more, for examining trends over time.

8 responses to “Climate and Weather Extremes

  1. 200-300 years or more Bryan? How much more?

    There is one area where it would be astonishing if there weren’t a signicant increase and that is exteme heat. Its a degree warmer, probably more, than 150 years ago and probably accelerating. https://tamino.files.wordpress.com/2019/11/era5-1.jpg?w=500&h=333
    That is going to see a continuation of this:

    How can it not?
    But you want to wait 300+ years? C’mon.

    • Tony,

      What I am discussing is extreme weather and in the last paragraph I am referencing the local scale. If we examine one site to represent a local area, then the highest temperature measured in the most recent 30-year period might be expected to reoccur once every 30 years or more, but we can’t be sure how much more with just 30 years of data. If we have 60 years of data, we can compare the peak measured in the first 30 years versus the peak measured in the last 30 years. We can have more confidence that the lowest of the two peaks will recur again in the next 30-year period, but the highest might have a longer return period. Similarly, if we have 90 years of data we can compare the peaks from three non-overlapping 30-year periods. In this case, the average of the three peaks would give us a better idea of what to expect for the next 30-year period, but only for a once in 30-year event.

      Now let’s go to a 150-year period. We can divided it into five 30-year periods and find the peak in each of those periods. The average would give us a much better confidence for what to expect in the next 30-year period and with five data points we can try to crudely evaluate a very low confidence trend. With a 300-year period we would have 10 data points for evaluating a trend at double the confidence of the 150-year period. We still would not have enough data to evaluate once in 500-year events or once in 1,000-year events without resorting to extrapolation and much larger uncertainty.

      I also revised the second paragraph in the post to help make it more clear. Hopefully I have better explained my logic in this comment. Please let me know if I am missing something.

  2. Forgive me if we are merely talking at cross-purposes, but it seems you are advocating for a wait and see policy. I guess my point is that what you say is true for all extreme events, except extreme heat. There is still plenty of uncertainty surrounding rainfall, cyclones, droughts etc and how each region may or may not be affected. The same cannot be said for extreme heat.
    The warming trend is already crystal clear and estimating future extreme heat events is the least uncertain aspect of climate change. The bell curve is going to predictably shift:

    It is already fairly certain how many more days over 35C per summer we’re in for. We do not need to wait 100 years to find out for sure whether mitigation is desirable. I live in an already hot place and I can tell you right now – its desirable.

    • Tony, it’s not clear if the graph you presented is based on actual data or on climate model expectations. It definitely is not for a specific site or location since it is labeled “Northern Hemisphere”. My discussion is focusing on local site measurements and how they affect global perceptions of extremes.

  3. The issue I have Bryan is your “200-300 years or more” statement. It suggests you think that in 300 years time we’ll be looking back saying “meh, see nothing burger”. In other words the best course to take right now is to do nothing.
    Elsewhere on your site you say: “the next glacial period, will be much more of a challenge for humanity than any small amounts of possible CO2 induced warming in coming decades.” So I guess you’re already saying “meh” if you think global cooling is a bigger problem. That graph I posted suggests otherwise.

    • Tony, the “200-300 years or more” applies to reliably determining measurement based statistics for extreme weather events. We can still speculate all we want about the future and maybe some day climate models will be reliable enough to help us do so. However, from what I have seen so far, our current weather and climate models can offer little more than speculation about the weather and thus climate beyond about a week or so. And yes, we should be careful not to be too short-sighted about trying to modify global climate given the context of climate over the last million years.

      From what I have seen, there is plenty of evidence that CO2 is a small player in climate change, but I don’t think it can be used as a “control knob” on climate. In my view, water in all of its various forms is far and away the dominant player for climate change over scales of decades, centuries, and maybe even millennia. Even if I’m wrong and CO2 can be used to control climate, prospects of greatly reducing fossil fuel related CO2 emissions on a global scale (as would be necessary) appear to be quite low and would involve drastic economic and environmental impacts – much more than if forced mitigation is not attempted. This of course is just my speculation. Your speculation may be different and you are entitled to it.

      Thanks for your comments. I do value your opinions and especially any constructive criticism. I try to keep an open mind and I have changed my mind about many things over my 67 years in this life so far. So I try to listen, look, and learn as much as I can.

      • Would you concede then that “reliably determining measurement based statistics for” heat extremes is easier than for the other types of “extreme weather events” given they are just slaves to temperature, not actually temperature – if we’re talking about detecting signals caused by anthropocentric emissions?

      • Tony, I have not attempted to statistically evaluate temperature extremes, so my discussion is hypothetical in that regard. But just out of curiosity, I happened to already have official US National Weather Service (NWS) temperature data for the Austin, Texas area, including means and extremes as well as station history. So I took a quick N=1 look at a period where the site was at a fixed location (NWS Office at Austin Mueller Airport) and the measurement method was the same throughout the period (Stevenson Screen with min/max thermometer). The data cover a 52-year period from 1943 through 1994. I graphed annual maximum, average, and minimum temperatures for the period and had Excel calculate linear trends for each. Below are the results in degrees Fahrenheit and expressed as temperature trend over 100 years.

        Maximum: -1.69 +102.9 R-square=0.0117
        Average: +1.26 + 68.0 R-square=0.031
        Minimum: +4.59 + 31.6 R-square=0.0143

        As you can see, at least in this case, none of the trends match each other very well. The highest temperature each year trended downward slightly, while the average temperature trended upward slightly and the minimum temperature trended more strongly upward.

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