Who’s to Blame for our Changing Climate?

The term 'smoking gun' is often brought up in reference to climate change, a quick google search reveals that this phrase has been thrown around in climate circles at least for the last 20 years or so. Often, the 'smoking gun' is a reference to some single, unrefutable piece of evidence that might finally silence climate change deniers, such as the rising levels of CO₂ (e.g. by Julia Slingo, Chief Scientist of the Met Office). However, for most people carbon dioxide levels in the atmosphere are not particularly tangible while, for example, the floods afflicting the south-west of England last winter or the record summer seen in Austria and Slovenia are much more visible and closer to our everyday experiences. Attributing events like these to climate change is not always simple though; after extreme weather events there may be debate regarding whether the event (or the scale thereof) can be attributed to the effects of climate change; perhaps these might just be part of natural climate variability? Such discussions rarely result in any kind of satisfactory answer for the media and, I suspect, the general public. The reason for this is not, as commonly claimed, that a single event cannot possibly be attributed to any root cause (although this is largely true) but rather that natural climate variability and climate change are not separate. Any trend in overall climate variables (e.g. temperature) will underlie the natural variability and it is this that makes global warming so dangerous. It has been repeatedly said (largely as a joke) that an extra degree or two might make the weather in [insert country/state/county here] more bearable. However, this simplification of the global warming trend discounts the variation which has existed and would exist without any warming (or cooling) trend.

An increasing temperature moves climate variability with it.

In this image (taken from climatecommunication.org) you can see how temperatures vary around a central, average temperature^*. A shift in average temperature (which is what climate change/global warming implies) shifts the entire distribution to the right, i.e. towards hotter temperatures. That means that weather events that might exist in this portion of the plot...

  ... which were once the extreme end of the distribution, now become far more common. So attributing a weather event to climate change means that we are saying it falls in the red part of this inset plot, rather than the orange. We are not able to definitively do that. What we can do is measure the number of times that extreme events occur and see how that compares with our plot of variability. A new record temperature is bound to occur at some point, when a record temperature is reported as being a 1-in-1000 year event, that means we only expect a temperature that high to occur once every thousand years. If a temperature that high were to happen tomorrow, it is possible (likely even) that it was just random chance that it occurred when it did. However, if it happened again the month after, that looks a little suspicious. If we were to reach that temperature again in two years, then again in another 10, then we begin to cast real doubt on our definition of 1-in-1000 year event. Either our statistics and/or model were wrong in the first place, or the system has changed.

The evaluation of how often certain weather events should occur is a type of risk analysis. By analysing the number of times that events occur, we can say how likely they are to happen in the future. Given enough data, we can even say what the contributing factors to those events are. For example, the NHS and other medical institutions can evaluate the risk of developing lung cancer. Given data about the lifestyles of the people who do develop it, it is possible to draw correlations between factors such as smoking and the incidence of the disease. After further investigation it is possible to more firmly establish these links and therefore we can say that there are different risks of lung cancer for smokers vs non-smokers and what these risks are. The important thing to remember here is that these are probabilistic risks, we have all had a great aunt or other relative who smoked 80-a-day and lived to a ripe old age. At the same time, there are many unfortunate people who live exemplary, healthy lives, who will contract lung cancer nonetheless. These people represent the natural variability of this system, while the people who smoke have shifted the distribution of probability towards contracting lung cancer.

Having described this kind of analysis in perhaps too much detail, I can get to the point of this post - the study by Sophie Lewis and David Karoly, researchers at the University of Melbourne in the overwhelmingly appellated 'School of Earth Sciences and Australian Research Council Centre of Excellence for Climate System Science'. They have performed an analysis like that I've described for the extreme summer of 2013 in Australia. I'll link to the paper itself here, published in the journal Geophysical Review Letters, although I'm not sure about paywalls, etc. - apologies if it's not readily available to you.

Lewis and Karoly performed an extensive analysis using suites of models to determine exactly how likely the extreme heat seen in the summer of 2013 in Australia would be in the natural (no human contribution) course of events and then again with human contributions included. They extended this further to include the RCP8.5 emission scenario (covered in a previous blog here) running forward to 2020.

The Australian 'Angry Summer' of 2013 saw record-breaking temperatures on a daily, as well as seasonal basis with the all-time record holders for hottest day and hottest month occurring. By running large numbers ('ensembles') of climate models, some of which included human contributions to emissions and some which didn't, Lewis and Karoly were able to evaluate the probability that these contributions would result in such an extreme summer. In addition to their paper, the authors have published two blogs which sum up their findings very well here and here. Here they publish their plots which illustrate their findings that human contributions have increased the likelihood that the 'Angry Summer' would occur by a factor of five. The plots below show how models incorporating natural as well as anthropogenic contributions reveal dramatically increasing probabilities of raised temperatures when evaluated from 2006 onwards.

Probability distribution of average temperaturevariations across Australia in summer from observations (dashed line) and climate model simulations (solid line) for 1910-2005. The vertical lines mark the temperature departures for 1998 summer (the second hottest) and 2013 (the hottest) summer across Australia/ Lewis & Karoly

As above, but showing the shift in the probability distribution for 2006-2020 from climate model simulations including increasing greenhouse gases and other human influences on climate. Lewis & Karoly

It is worth digging into these results a bit, they are explained thoroughly by the authors in the paper and summarised well in their blog postings so I'm not going to repeat what they say. What is worth showing here is the spread of their model results. I think the plot below shows something that is often missing from statistical reports, climate or otherwise.

Australian annual temperature changes (relative to 1911-1940 average) for observations (dashed black) and model simulations with natural influences only (green) and with both human and natural influences (red). The grey plumes indicate the range of values simulated across nine global climate models used. Average Australian temperature anomalies are indicated for 2013 and the previous hottest year on record in 2005. David Karoly & Sophie Lewis

What this plot shows is not only the results from the various models (green showing climate variability arising from natural contributions only, red including human emission contributions) but also what the spread in those models looks like (in grey). This is very important as it is easy to see from the variation in observed temperatures that, for any given year, the red line and green line aren't really separated by more than we might expect from natural variations anyway. The grey spread of model results shows us that the green line, representing the 'natural' state, is now right on the edge of the feasible range predicted by our models. This means that we are now entering a period in which it is impossible (statistically) to account for current weather trends without incorporating the influence of human emissions. Australian Prime Minister Tony Abbott is fond of quoting the poet Dorothea Mackellar in her description of Australia as 'a land of droughts and flooding rains' in dismissing possible climate change. However, it has become completely untenable to ignore the changing climate in that country. Climate change deniers, who once might have charitably been called skeptics have descended into the realm of conspiracy theory. I won't link to any sites because I'd rather not give them any traffic but it is all too simple to search online (or simply look in the comments of legitimate blog posts) for climate change in Australia and find sites, no longer able to refute scientific findings, which now simply accuse scientists of falsifying data, proactively as well as retroactively.

One of the more legitimate plausible explanations for high temperatures in Australia is the El Niño Southern Oscillation (ENSO), which has been regularly linked to higher than average temperatures in the Pacific. It is true that the second hottest summer in Australia to date (1998) may well owe some of its heat to ENSO. However, 2013 was essentially an 'ENSO - neutral' year and so the record temperatures were almost certainly unaffected by it.

One last thing to mention about Australia's extreme climate (changing or not) is the absolutely phenomenal amount of rainfall experienced there in the last few years. In the two years preceding the 'Angry Summer' Australia was subject to exceptionally heavy rainfall, this time perhaps linked to an El Niño/La Niña event. While attributing this heavy rainfall to human influences is more muddled than with the record temperatures, I reiterate my earlier point that we can no longer take 'natural variability' in isolation from anthropogenic global warming. My main reason for bringing the rainfall is that I was struck by the fact that so much water fell on Australia in those two years that sea levels ceased to rise. Andrew Freedman blogs here in detail about this topic, the main gist being that the 3.2 mm/year sea level rise that has been observed for decades plateaued for an 18 month period correlating with the rains falling in Australia. The explanation posited in this study is that the particular geography of Australia prevented much of this water returning to the oceans on short timescales - therefore taking water from the oceans without returning it.

^*This is a bit simplified, this temperature distribution shows an essentially Gaussian distribution. There are good reasons why real temperature distributions might not be Gaussian but that's another story for another time... The general principle here will still stand.