I wish I could have filmed my recent trip to the National Climate Data Center in Asheville. I met with four scientists with differing specialties: ocean currents, meteorology, quality control, and educational outreach. The conversations did not at all go in the direction I thought they might. We talked some about the normalization of data, how, for example, data is adjusted to consider the amount of impermeable surface or albedo at a collection site. One of the things I had not thought about is that most data collection sites are airports. Airports are usually built in a relatively rural area (think RDU…) and the cities and towns grow up around them and thus change the baseline for temperature data. So good climatologists have to adjust these data over time in an accurate way. So that’s one of the things that often comes into the discussion when climate change is questioned. How accurate are the models we use to adjust these values? (Pretty darned accurate, actually…)

I think the funniest comment was that climatologists are often accused of being in a giant conspiracy to bilk world-wide governments out of money. One of the scientists grumbled that “three people can’t even keep a conspiracy quiet. How would the entire world-wide community?”

The biggest A-Ha moment may not seem like much, but it really is, in the climate change “debate.” Weather reporting in the US and worldwide are biased toward what happens on the east coast…because that’s where Washington DC is. Having recently spent some time in the midwest, this is totally true. So anytime the weather in Virginia doesn’t quite adhere to a suggested or assumed trend, we hear all about it. (I was in Europe in December and only remember reporting on Virginia’s weather while we were there!)

We talked about how to talk to my students about bias in statistics on blogs and elsewhere. And we pretty much decided that the “Skeptical Science” site from Australia is pretty darn good!

Well worth the drive…

When I first started teaching, I had a long conversation with my parents. Both of them taught for long, successful careers. I talked about the new challenges for getting kids through school, ready for college: computer literacy and modern pressures to excel. The crux of the conversation was that the overall expectations for students are the same as they were when I was in high school. You need to be able to write well, do at least applied simple mathematical and graphing functions, and think about what you’re arguing and why.

Common core has less required information, at least in the science disciplines that I teach. Some of the required curriculum is enhanced by the use of technology: it’s easier to see Milankovitch cycles using an animation model rather than using physical models with globes, etc. The cycles are very long and precession and nutation are slight compared to the scale of the universe. (Milankovitch cycles are more or less new to the Earth Science curriculum.)

It’s easier for students to get sidetracked by technology and miss the important message or significant skill taught in a lesson. There’s so much available information on the internet. It’s so much easier for students to find poor-quality information. So teaching critical thinking can be really challenging in the modern world.

The unit plan I am authoring for Kenan includes background information about climate change, specifically from professionals researching and using climate information; the use of statistics in climate science and some reasons why climate statistics and interpretation are sometimes considered controversial in the lay community; an understanding of line of best fit and the concept of regression; hand calculations of the value of r² as a measurement of the relationship between values in a data set; use of statistical freeware such as R; using public databases to cull relevant climate data; correctly extrapolating and interpolating values on a graph using r².

My summer internship requires that I work with two statisticians from SAMSI to teach an aspect of climate change in the classroom and how statisticians make decisions using data. My kids get distracted by outliers and incongruent examples. I hope to use regression to demonstrate how to better understand the meaning of datasets.