February 22, 2014 — andyextance

It’s all very well to read about climate change – but you can probably get a better understanding from actually exploring the data and underlying physics yourself. That’s been driven home by some recent comments on this blog by non-scientist readers wanting to do just this, or recommending that I do. Inspired by them, in this week’s blog entry I’m bringing together various different ways we can all do this. Don’t worry, I won’t tax any weary brain cells any more than they want to be. I’m organising the blog entry in order of increasing effort/difficulty – just bail out or take a break whenever you need to.

As a simple starter, try the Carbon Quilt tool that lets you see your CO2 emissions. If you click on this link or the image above you should first see the size of a ‘quilt’ or ‘patch’. That represents the average amount of CO2 people in your country emit, overlaid on a map. Try out the sphere and cube options, and the different options in the drop-down menu to see how big your carbon footprint really is.

Another simple but powerful demonstration is the Guardian interactive guide to how warm it will get in our lifetimes pictured above.

Still more powerful, I think, is this guide showing the significance of CO2 levels in the air hitting 400 parts per million last year.

Turning the dials

Pretty and easy-to-use as these tools are, they’re very much a ‘black box’, something we have to trust the invisible inner workings of. But there are resources out there that help us get under the surface. For example, last Friday, Robin Curtis asked me if I’d ever seen a ‘simple animated or spreadsheet based model’ showing increased CO2 levels in the air caused warming. He wanted to ‘twiddle the basic parameters’. I pointed him towards NASA’s Global Equilibrium Energy Balance Interactive Tinker Toy (GEEBITT), a spreadsheet climate model aimed at school classrooms.

The GEEBITT spreadsheet that’s fastest and easiest to use but features a greenhouse effect also includes equations that calculate other key elements controlling a planet’s temperature. They are the energy produced by the sun, the planet’s distance from the sun and the ‘albedo’ of the planet’s surface, the measure of how reflective it is.

The spreadsheet has detailed instructions, but you can just flick through the different sheets by clicking on the labelled tabs you’ll see at the bottom. Typing different numbers in the grey boxes will change the temperatures in the blue boxes. You can access the spreadsheet in your browser here or download an Excel file here. If you want to get deeper still, looking at clouds, soot and different greenhouse gases, there’s a GEEBITT Excel spreadsheet that will do this here.

Delayed consequences

Even in these models the underlying physics and maths are hidden, but another schoolroom-targeted spreadsheet tries to show the workings behind an important part of climate science. This time it’s from climateprediction.net, a collaboration between the Open University, University of Oxford, and Rutherford Appleton Laboratory in the UK. Its scientists primarily want to borrow your computer’s idle time to help their climate modelling experiments, answering tough questions like whether global warming is causing the current weird weather. Whether or not you want to look deeper beneath the surface of their models, you could consider installing their software to help with that.

The climateprediction.net exercise looks at an issue glossed over in the GEEBITT models – that the energy entering and leaving Earth’s atmosphere doesn’t immediately balance out. Its spreadsheet models what happens when you have a sudden jump in energy arriving from the Sun. It simply asks you to take the bottom row of numbers in the spreadsheet and copy it into at least 20 rows underneath. You can access their original Excel version here, and my version that you can view in your browser, has more explanation and a tab with the final result here.

Using four equations explained here, the spreadsheet shows that the planet takes time to warm up to a level where incoming and outgoing energy balance. This is what we’re experiencing today, only rather than changing the energy from the Sun the ‘quilt’ of greenhouse gases we’re emitting is stopping energy leaving the atmosphere. But because the planet warms up slowly, even if we stop emissions today we would still be set to heat up more to reach a balance.

Is it hot or not?

Still with me? Good, then I’m going to take this opportunity to rise to a challenge set for me by a climate skeptic, rogerthesurf, commenting on this blog entry. “Do you know that if you take NOAA’s data for the last 15 years or so, and do a ‘least squares regression’ using Excel you will find that any warming is less that the error coefficient,” he wrote. “I won’t give a reference for this fact, perhaps you should do it for yourself.”

Rather than take you through where I got the data from and how I used it here, you can see what I did in this spreadsheet. Roger’s trying to point out that the statistical uncertainty in the warming trend is greater than the warming itself. He’s right for the 1998-2013 period, of course, but there are some points worth remembering about why that is.

First, 1998 was the warmest year of the 1990s – as warm as 2013 – with the effect of the natural El Niño cycle piling on top of CO2-driven warming. So with no temperature difference between 1998 and 2013, any standard error would always be larger. But my spreadsheet shows warming is larger than the standard error for the periods 1997-2013 and 1999-2013, which slightly dents Roger’s argument. And scientists would generally argue that you can’t draw meaningful conclusions from a 15-year data set anyway. “A 15 year trend is still too short to be considered as representative of longer-term global temperature trends,” said the University of Reading’s Ed Hawkins recently.

Interested in taking things further still? Well, sorry, that’s as far as I’m going. If you’re reading this on an Apple Mac, perhaps you might want to try this app of the kind of global circulation model climate scientists regularly use. Made by Brad Marston from Brown University in Providence, Rhode Island, I think it’s supposed to have help for first time users, but without any Apple products I can’t use it! In any case, well done for getting to the end of this ‘datahead’ session. I hope these tools have been of some use and/or interest. If anyone knows of anything else similar, please share in the comments below.