Crowd-sourcing Cherries

I recently came across a “quiz” produced by Richard Keen, which ended with this graph:

It’s Global Warming, Stupid

Here (shown as black dots) is the total acres burned by wildfire in Washington state each year, together with a blue dashed line showing the yearly average, since 2002:

Cliff Mass: Picking Cherries in Full Denier Mode

I wonder whether Cliff Mass can comprehend what he himself has done?

FIRE

Matt Pearce wrote an article for the LA Times in which he makes it sound as though the wildfire situation in the western U.S. is no big deal. He does so by pointing out that most of the area burned in the U.S. this year is in Alaska. I guess according to Matt Pearce, Alaska either isn’t part of the U.S. or it just doesn’t matter.

Hottest Month

Now that NASA has released their data updated through July, we know that in that data set, this July was the hottest July on record with a temperature anomaly of 0.75 deg.C, i.e. it was 0.75 deg.C above “climatology” (which is what’s usual for the given month). It’s not the hottest temperature anomaly in the data set, however; that record still belongs to January 2007, at 0.96 deg.C above climatology.

Yet it does seem that this July, while not the hottest temperature anomaly on record, is the hottest month on record.

Hellfire

Fires which burn 1,000 acres or more are considered “large.” That’s well over a square mile (1.5625 to be precise). Imagine a wildfire consuming an entire square mile — it’s huge, clearly a devastating conflagration.

So when I learned that the “Rocky fire” in California had burned through more than 100 square miles, I knew it was huge beyond huge, the biggest fire I’d ever heard of. It’s the kind of raging inferno that costs so much money, property, even lives at times, that takes so many resources to fight, it’s more than just a drain on resources, it’s a disaster, at least for those who live in the vicinity.

What’s Beef?

Survival, part III: the program

Note: See the update at the end of the post.

Since I’ve devised a new way to estimate the survival function, I wrote some code (in R) to execute it. The program is in its early stages of development, so there’s a lot of work still to be done. But one of the ways to learn about it is to let people play with it. So, I’ve decided to share my code so others can have some of the fun.

Survival, part II

In the last post I mentioned that I’ve come up with a new way to estimate the survival function. I first got the idea years ago while studying survival analysis. But recently I became aware just how troublesome it is to get good estimates of confidence limits, so I elaborated on the idea and came up with what I think is the answer.

Of course I could be wrong. And of course it’s possible somebody else already thought of it, and it’s published somewhere I’m not aware of. That’s the way science works. But I have submitted a manuscript for publication, so I should find out before too long. In the meantime, I’ll share it here.

Survival, part I

The topic: it’s probably not what you think.

This post has nothing to do with climate change. It’s about the survival function, which finds application in many fields, most notably medical research (where survival is considered important) and failure testing. I’ve come up with a new way to estimate it from observed data, and most important, what I believe is a much-improved way to estimate confidence limits.