by Judith Curry
This article aims to portray and communicate the important role played by natural variability in our evolving climate. Understanding and acknowledging these variations is important for society and policymakers. Much of this variability is chaotic and unpredictable but some significant fraction is potentially predictable, providing an opportunity to narrow the uncertainty in climate predictions of the coming decade.
The above quote is from the following article:
Our evolving climate: communicating the effects of climate variability, by Ed Hawkins, published in the magazine Weather. Link to the complete article [here] and [here]. These two versions are slightly different; my quotes are from the actual article published in Weather.
And Ed has a blog: Ed’s Open Lab-Book. I very much like his blog and have added it to my blog roll. Check it out.
IMO this article is climate science communication at its best. It provides the appropriate perspective in terms of complexity and uncertainty, while at the same time relating to his audience, clearly explaining the issues, and creating useful analogies. The entire article is well worth reading. Some excerpts from the article:
However, when presenting this range of responses, the IPCC tends to show the average and spread of the GCM projections, indicating a relatively smooth increase in temperatures over the coming century (Fig. 1b, blue shading). Although this representation provides a range for the likely increase in future temperatures, it tends to disguise the natural variability of climate. One particular projection illustrating the impact of internal climate variability on EU temperatures is shown in Fig. 1c. To highlight the importance of the natural fluctuations in climate, a decade which shows a sharp decline in temperatures is then chosen from this particular projection (Fig. 1d), demonstrating how a climate trend may be misrepresented when considering a relatively short time period. Although this particular decade is chosen specifically, it is not unusual – there are also several periods of rapid warming and rapid cooling in the observational record.
So, what is the chance of one year being cooler than the last? For global mean temperatures in the future, there is roughly a 40% chance that one year will be cooler than the last (Fig. 2a). Equivalently, this could be expressed as `2 in every 5′ years. For smaller regions, such as Europe or the UK, this chance is higher (around 47%). Although this may seem quite counter-intuitive, there is not much difference between the chances of a head or tails when tossing a coin, and whether the temperature in Europe one year will be warmer or cooler than the last.
However, for longer timescales the odds change because of the gradual upwards trend in temperatures. Decades which exhibit a cooling (or a negative temperature trend) are only expected occasionally in the future for the global mean (about 5% of decades), but these chances increase to 24% of future decades for Europe and 36% of future decades for the UK (Fig. 2b). Expressed as odds, there is roughly a 1-in-3 chance of a particular future decade exhibiting a cooling trend for the UK. This is a key point which is essential for society and policymakers to appreciate – temperatures are expected to (temporarily) go down as well as up, even in a warming climate.
Another interesting question to ask is, how long might we have to wait before a warmer year occurs? Although, we may be surprised that 1998 is still the warmest year recorded, the GCMs suggest that, for global mean temperature, it is possible that we could wait 17 years; and so far we have been waiting 12 years. For smaller regions, climate fluctuations are larger and for UK temperatures, we could wait nearly 50 years, although usually it would be under 5 years.
Although there are caveats to this simple analysis, it demonstrates how climate variability is likely to affect an individual’s interpretation of whether there are long term changes in climate or not. To determine whether the climate is truly changing, it is essential to consider long (multi-decadal) timescales and large spatial scales.
I really like this one:
DEMONSTRATING CLIMATE VARIABILITY AT HOME
Take a shuffled pack of playing cards, with red cards representing ‘warm’ years and black cards ‘cool’ years. When dealing the pack there will be times when several warm or cool years appear together. Next, remove some black cards from the pack, and reshuffle. This pack now represents a changed ‘climate’ with less cool years. When dealing the pack for a second time, there will be more periods of warm years, but probably periods of cool years as well. Even though the climate has warmed, every year need not be hot!
JC comment: IMO this is much better than the “loaded dice” analogy that is often used.
Variability as an analogue for the future. As described above, our climate is changing relatively slowly compared to human memory. As significant climate changes tend to only appear after many years, it can be hard to appreciate how the climate has already changed, and hard to imagine the impacts that are projected to occur; the climate in the 2050s may seem quite a remote prospect. However, natural climate fluctuations can help us appreciate what living in a changed climate would be like by acting as an analogue for what the future holds.
Variability as uncertainty. Although climate scientists are confident in the direction of any long term change in temperatures, there is a range of estimates for the magnitude of the change, i.e. some uncertainty. However, there are several different sources of uncertainty in our projections of climate. As indicated previously, the climate we will experience in the coming decades is significantly influenced by natural fluctuations, giving rise to some uncertainty over the trajectory the climate will follow (the internal variability component of uncertainty). There is also uncertainty in future climate due to different GCMs having different responses to greenhouse gases etc (termed model response uncertainty, for example, as shown by the spread in projections in Fig. 1a), and also uncertaintyin the rate of future greenhouse gas emissions (termed scenario uncertainty). The potential to narrow uncertainty in climate projections depends on which source of uncertainty is dominant. For example, climate science can tackle the model response uncertainty, but can do little to reduce the uncertainty in future emissions because this depends on economic development and human behaviour.
Predicting a decade ahead. The inset in Fig. 5 shows individual forecasts started in 2005. Although the subsequent observations are generally within the range of predicted temperatures, they are at the lower end. This could have happened for at least three possible reasons, (i) by chance, or (ii) because the model is not able to fully predict the natural variability (such as the 2008 La Nina), or (iii) because there are other external forcings which are not yet fully accounted for in the model.
JC comment: there is also (iv) model inadequacies associated with treatment of physical processes, e.g. aerosol effects, solar indirect effects, formation of clouds, etc.
Numerical weather prediction has benefited from continually assessing the ability of the computer models to make forecasts. Further testing of our climate models in a similar way is vital to increase confidence in their use for longer term projections and potentially identify parts of the model which require improvements.
JC comment: This is a very important step in validating and improving climate models. In addition, arguably the most important potential applications of climate models are on regional spatial scales and decadal time scales. Focusing on decadal time scales with a good validation strategy should be top priority in climate modeling, IMO.
JC conclusions: IMO this article does an excellent job in explaining the challenges associated with understanding AGW against a background of interannual and multi-decadal climate variability and also regional variability.
The need for better understanding of climate models and how their simulations should be interpreted is illustrated by this article at the Yale Forum entitled “The Case of Missing Coverage of Models” (h/t Bill Collinge, I missed this when it first came out.)