Seeing the environmental forest

 I've created a web-based app that calculates linear regression trends on the Cowtan-Way global temperature data set  using annual mean temperature. The reason I picked that particular data set is simply that it's one of the easiest of the surface temperature data sets to download to R.  I chose annual data because red noise is insignificant at that scale so we can go with linear regression without worrying about autoregression. The app was made using the Shiny app in R Studio. When you open the app, you're greeted by a single page with two input boxes on the left-hand side. You enter the start year in the top box and end year in the bottom box and the Shiny app does the rest. The output includes the linear temperature trend per 100 years, the 95% confidence interval for that trend, a graph of the data and trend, complete with 95% confidence interval lines, and at the bottom the actual R output listing the model and fit statistics like the R 2 statistic.  I've demon...

 It's been a few years (close to four) since I last wrote a post. Yes, I'm still alive. Life just...got complicated for a bit. While much has been happening, it seems that outright climate change denial is finally dying, probably because we're already seeing it happen before our very eyes. In this post, I'm revisiting the Shifting Bell Curves I first wrote about in 2013. Here we go. One of the tenants of climate change is that it changes the frequency and probability of climate-related events. Here I use NCDC data broken into decades to show how the frequency of monthly global mean temperatures changed over time. We're all familiar with time series plots of temperature data such as the one below showing that global mean temperatures have risen by an average of 0.179ºC per decade (95% confidence interval: 0.171 to 0.188ºC) since 1970. However, this way, while still informative, doesn't allow the reader to really grasp just how much the distribution of monthly me...