Appreciating the reality of climate change can be hard. As something that manifests itself most clearly over decades and large areas, perceiving – and perhaps accepting – climate change requires engagement with scales beyond our immediate experience. Long-term change is easily lost in the day-to-day variability of weather, and global scale signals might not be obvious from one’s locality.

One way to engage with change is to look at measurements taken over a long period. We can put “hard facts” to the past and locate it in the context of our more recent experience. If the past is part of our lived experience, we might be able to expand on a particular event and render it more tangible than just a number.

As part of thinking about local climate change, I have been analysing weather data from Lancaster University’s Hazelrigg weather station. This site has been active since late 1966, although having to move (and therefore impacting trends!) in 2012 when Lancaster’s wind turbine was installed. I am grateful to site manager James Heath for providing the data, and to his army of volunteers who take the readings every day.

Inspired by Edward Tufte’s books and climate data visualisations from people like Ed Hawkins, I have been thinking about how we might visualise the Hazelrigg record in ways that might help us see the short term changes, particular events (e.g., heatwaves and cold spells) and the long term trend. To that end, I have produced a set of visualisations from the daily maximum and daily minimum temperature data, which are shown below.

I have tried to present the data in a way where we can see daily, seasonal and yearly patterns and variability. For instance, the simple cold/hot pattern of winter and summer are clearly visible in each year’s cycle. We can also see the hot episodes in 1976, 1995, 2006 and 2018, and the cold episodes in 1969, 1981, 1993 and 2010. There are no doubt numerous people living locally with stories to tell about these events.

What about longer-term changes? The anomaly plots (difference from a long term average; our “expected” temperature) show that day-to-day we can be warmer and colder than normal. It’s not until we aggregate to get an average yearly anomaly that the long-term signal of climate change emerges. There are meanders in these yearly average anomalies year-to-year, but a warming signal is clear in both the minimum and maximum temperature data: each increases by more than 1°C over the 53-year record. (Although, again, we must caveat our trend analysis with the disturbance caused by the wind turbine installation.)

The analysis can be extended to other weather variables (e.g., sun, rain, cloud cover etc.) to provide a fuller picture of the area’s climate and how it is changing. One idea is to use the data to enrich predictions of possible future climates, generating relatable “stories” of what-could-be if the world warms by 1.5°C, 2°C or more. Another extension would be to put Lancaster’s weather in the context of regional, national and global changes. The relationship across those scales is not straightforward, and – for instance – one can demonstrate that there are possible futures where the global warms but local temperatures stay more-or-less the same.

There is nothing particularly fancy about the data analysis here, just a fair amount of effort to code the reading of the data and the production of the visualisations. What I am interested in pursuing is whether seeing the data from day to year, and then to multiple decades helps us rationalise and internalise the climate change signal? Does it make climate change more visceral to see it in a place that we know well, perhaps having lived here a lifetime? And then, what would we do with the knowledge that our climate is changing?

High resolution images of these plots are available by contacting Paul Young

Related Blogs

• 

  Annual meeting of the Society for Decision Making under Deep Uncertainty, 2022

• 

  The Cryosphere 2022 Conference

• 

  DSNE Reseachers receive best poster award at CDE Conference 2022

Disclaimer

The opinions expressed by our bloggers and those providing comments are personal, and may not necessarily reflect the opinions of Lancaster University. Responsibility for the accuracy of any of the information contained within blog posts belongs to the blogger.