About a year ago, I thought that Climate Change Deniers had lost the argument.
I thought that we were all moving on to answering more interesting questions, such as what to do about it.
I am left speechless in the face of this kind of intellectual dishonesty.
Actually I am only almost speechless. I intend to continue trying to empower people by fighting this kind deception.
Rather than trying to woo people over to my view, my aim is simply to offer people the chance to come to their own informed opinion.
See for yourself
As part of my FREE University of Chicago Course on Global Warming, I have been using some astonishing FREE software. And its FREE!
The ‘Time Series Browser’ allows one to browse a 7000 station subset of our historical temperature records from meteorological stations around the world.
- The data are the local station temperatures averaged over 1 month, 1 year or 1 decade. Whichever you choose you can also download this data into a spreadsheet to have fun with on your own!
- One can select sets of data based on a variety of criteria – such as country, latitude band, altitude, or type of geographical location – desert, maritime, tropical etc. Or you can simply pick a single station – maybe the one nearest you.
Already this is enormously empowering: this is the pretty much the same data set that leading climate scientists have used.
For this article I randomly chose a set of stations with latitudes between 20°N and 50°N.
The bold dots on the map show the station locations, and the grey dots (merging into a continuous fill in parts) are the available locations that I could have chosen.
The data from the selected stations is shown below. Notice the scale on the left hand side runs from -10 °C to + 30 °C.
In this form it is not obvious if the data is warming or cooling: And notice that only a few data sets span the full time range.
So how do we discover if there are trends in the data?
The first step
Once you have selected a set of stations one can see that some stations are warm and others cool. In order to be able to compare these data fairly, we subtract off the average value of each data set between 1900 and 1950.
This is called normalisation and allows us to look in detail at changes from the 1900-1950 average independent of whether the station was in a warm place or a cold place.
Notice that the scale on the left-hand side is now just ± 3.5 °C.
The second step
One can then average all the data together. This is has the effect of reducing the fluctuations in the data.
One can then fit a trend-line to see if there is a recent warming or cooling trend.
For this particular set of stations its pretty clear that since 1970, there is a warming trend. The software tells me it is approximately 0.31 ± 0.09 °C per decade.
What I have found is that for any reasonably diverse set of stations a warming trend always emerges. I haven’t investigated this thoroughly, but the trend actually seems to emerge quite clearly above the fluctuations.
But you can check that for yourself if you want!
Is it a cheat? No!
You can check the maths of the software by downloading the data and checking it for yourself.
Maybe the data is fixed? You download the source data yourself – it comes from the US Global Historical Climatology Network-Monthly (GHCN-M) temperature data-set.
But accessing the raw data is quite hard work. If you are a newbie, it will probably take you days to figure out how to do it.
There is more!
This ability to browse, normalise, average and fit trends to data is cool. But – at the risk of sounding like a shopping channel advertorial – there is more!
It can also access the calculations of eleven different climate models.
For the particular set of stations that you have selected, the software will select the climate model predictions (a) including the effect of human climate change and (b) without including human-induced climate change.
For my data selection I chose to compare the data with the predictions of the CCSM4 Climate model. The results are shown below
You can judge for yourself whether you think the trend in the observed data is consistent with the idea of human-induced climate change.
For the particular set of stations I chose, it seems the CCSM4 climate model can only explain the data by including the effect of human-induced climate change.
But Michael: this is just too much like hard work!
Yes and no. This analysis is conceptually challenging. But it is not crazily difficult. For example:
- Schoolchildren could do this with help from a teacher.
- Friends could do it as a group and ask each other for help.
- University students could do this.
- Scout groups could do it collectively.
It isn’t easy, but ultimately, if you really want to know for yourself, it will take some work. But then you will know.
[January 28th 2017: Weight this morning 71.2 kg: Anxiety: Sick to my stomach: never felt worse]