By: David Brayshaw
In a few weeks’ time, I’ll be taking some time off for an adventure: spending 3-weeks cycling the entire 3,400 km of this year’s Tour de France (TdF) route. I’ll be with a team riding just a few days ahead of the professional race, aiming to raise £1M for charity. Although this is a purely personal challenge – unrelated to my day job here in the department – being asked to write this blog set me thinking about the connections between cycling and my own research in weather and climate science.
Weather is obviously important to anyone cycling outdoors: be it extremes of rain, wind or temperature. Cycling in the rain can be miserable but, more than that, it can lead to accidents on slippery roads and poor visibility for riders. Cold temperatures and wind chill pose challenges particularly when descending at speeds of up to 50 mph in the high mountains (in years gone by professional cyclists often took a newspaper from a friendly spectator at the top of a climb to shove it down the front of their cycling jersey to protect themselves from the worst of the wind chill). Air resistance and wind play a major role more generally: the bunching up of the peloton occurs as riders save energy by staying out of the wind and riding close behind the cyclist in front. While, while headwinds sap riders’ energy and lower their speed, it’s crosswinds that blow races apart. In that situation, the wind-shielding effect runs diagonally across the road, shredding the peloton into diagonal lines as riders fight for position and cover.
Photo: Grim conditions on a training ride in the Yorkshire Wolds, April 2023.
Last year’s TdF race, however, took place in a heat wave. The athletes did their work in air temperatures approaching 40 oC, stretching the limits of human performance in extreme temperatures. On some days the roads were sprayed with water to stop the tarmac melting (road temperatures were often closer to 60 oC), and extreme weather protocols were called upon (potential adjustments include changes to the start time or route, making more food and water available, even cancelling whole stages). All this comes with risks and costs (human, environmental, financial) for a range of people and organisations (the riders and spectators; the organisers and sponsors; and the towns and communities the ride goes through). Moreover, heatwaves can only be expected to become more common in the years to come.
From a meteorological perspective, the “good news” is that tools are available to help quantify, understand and manage weather risks. High-quality short-range (hours to days) forecasting is obviously essential during the event itself but subseasonal to seasonal (S2S) forecasts or longer-term climate change projections may also help to manage risk over a longer horizon (e.g., hire of water trucks, anticipating the need for route modification, use of financial products to mitigate losses if stages are cancelled or adjusted, even reconsidering the timing of the event itself if July temperatures become intolerable in the decades to come).
The specifics of the decisions and consequences described here for this particular race are simply speculation on my part (I have not done any in-depth research on climate services for cycling!). However, the nature of the “climate impact problem” should be familiar to anyone working in the field. As an example, some recent work I was involved in which produced a proof-of-concept demonstration of how weeks-ahead forecasts could be used to improve fault management and maintenance scheduling in telecommunications (see figure below and full discussion here), but many more examples can be found (see here for a recent review). In such work, there are usually two core challenges. Firstly, to link quantitative climate data (say, skillful probabilistic predictions of air temperature weeks ahead) with the impact of concern (say, the need to cancel part of a stage and the financial losses incurred by the host town that is then not visited). Then, secondly, to identify the mitigating actions that can take place (say, the purchase of insurance or a financial hedge) and a strategy for their uptake (say, a decision criteria for when to act and at what cost). The broad process is discussed in two online courses offered here in the department (“Climate Services and Climate Impact Modelling” and “Climate Intelligence: Using Climate Data to Improve Business Decision-Making”).
Figure: Use of week-ahead sub-seasonal forecasts to anticipate and manage line faults. Left panel demonstrates that predictions of weekly fault rates made using a version of ECMWF’s subseasonal forecast system (solid and dashed lines represent two different forecast methods) outperform predictions made using purely historic “climatological” knowledge (dotted line). The right panel illustrates the improved outcomes possible with the improving forecast information (from red to purple to blue curves): i.e., by using a “better” forecast it is possible to achieve either higher performance for the same resources, or the same performance for fewer resources (here as an illustrative schematic but an application to “real” data is available in the cited paper). Figures adapted from or based upon Brayshaw et al (2020, Meteorological Applications), please refer to the open-access journal article for detailed discussion.
For this summer, however, I’m just hoping for good weather for my ride. Thankfully I won’t be trying to “race” the distance (merely survive it!), so a mix of not too hot, not too wet, not too windy would just be perfect. With a bit of luck, hopefully, I’ll make it all the way from the start line in Bilbao to the finish in Paris!
If you’d like to find out more about my ride or the cause I’m supporting then please visit my personal JustGiving page (https://www.justgiving.com/fundraising/david-brayshaw-tour21-2023).
References:
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Brayshaw, D. J., Halford, A., Smith, S. and Kjeld, J. (2020) Quantifying the potential for improved management of weather risk using subseasonal forecasting: the case of UK telecommunications infrastructure.Meteorological Applications, 27 (1). e1849. ISSN 1469-8080 doi: https://doi.org/10.1002/met.1849
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