How Can Citizen Science Help Measure Climate Change?

Post by Prof Chris Brunsdon

Recently I visited the University of Minnesota at Minneapolis to attend a workshop on Understanding Climate Change Through Data Analysis. There were a number of very interesting talks and demonstrations of software to model and analyse climate change data.  From a personal perspective I had an eventful visit, when the plane I was traveling home on developed a fault in the navigation system – and had to turn back to Philadelphia.  

I was giving a talk about how Citizen Science – and in particular the USA National Phenology Network provides an interesting way of monitoring climate change.  A large number of citizens have collected data on the day in the year when they see the first bloom of a lilac in a particular place.  As people have been contributing this data since 1956, the idea is that it can be examined to see whether first bloom dates are getting earlier – something that might happen if, on average, temperature were increasing over time.

The results suggest that this is the case – to the tune of around a day every five years – one the geographical distribution of the contributors is taken into into account.  Since the USA is a relatively large place spring arrives in a graduated sweep across the whole of the country. Even without any ongoing trends in temperature you would expect people in some places to see first blooms sooner than others.  A map of this (also based on this data) is shown here; the red dots correspond to earlier first bloom dates –


If this wasn’t allowed for,  year-on-year changes might be attributable to changes in the location of contributors.  However,  the results here suggest that even allowing for this,  the trend is still there.

Perhaps one of the most interesting things here is that the idea of Citizen Science – and particularly the public recording of environmental data – has been around for quite a while.  Although climate change was probably not on the minds of those original contributors in 1956,  their input has made it possible to analyse this data over a much longer timespan than would otherwise have been possible…


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