A precursor eruption of Pinatubo, June 1991 (USGS)
Large volcanic eruptions can affect global climate — ask the folk who starved to death in Europe in 1816, after the massive eruption of Tambora in far-away Indonesia the year before. Explosive eruptions often inject vast quantities of ultrafine material high into the stratosphere, where it spreads around the planet blocking incoming sunlight.
. . . → Read More: Volcanoes and snow
Bear with me here. I come from a place with a long history of nutty sunspot-based weather prediction, so I’m well aware of the pitfalls. But our sun really is a very slightly variable star, and aspects of our climate really do very slightly vary with its output — particularly snow cover.
. . . → Read More: Sunspots and snow
If you begin monitoring some variable thing over time, initially you’re going to see lots of new extremes — new record highs, for example. As your dataset grows, the frequency of new ‘records’ should decline sharply. If the overall system behavior is static, new records rapidly become very rare indeed, governed by the statistics of the variation. A feature of the 165-year long global temperature series in recent years is that new records highs have not been rare at all.
. . . → Read More: The frequency of ‘records’
Update: Added note on Berkeley Earth.
Both the NASA GISTEMP and NOAA NCDC global temperature series have updated for December, confirming that the year just completed was once again the warmest on record. That’s since 1880 when those series start, but really since at least 1850 when the other instrumental series begin
. . . → Read More: 2014 the warmest … again
Another way to look at snow cover is by satellite remote sensing. The longest and best-known series by far is that from the US Landsat satellites, now up to Landsat 8, launched in early 2013.
. . . → Read More: Landsat snow
Having taken the trouble to plot all eight popular global temperature series together on one graph at monthly resolution — something the other seven billion of you don’t seem to have bothered with — it may be fair to spare us the indulgence of a few simple observations
. . . → Read More: More thoughts on global temperature
You’ve got some data and think a Pearson type III distribution might fit it nicely, but how do you go about choosing the parameters? The obvious way — using the mean, standard deviation and skewness of the sample — is much frowned upon. That’s because it can give a biased fit, although in the real world it often performs well, as we’ll see. . . . → Read More: Fitting the Pearson type 3
We did type III, so what about the Pearson type IV probability distribution? One author calls the type IV a Cinderella distribution¹ — it’s a beautiful thing, but completely lost to most. . . . → Read More: Pearson type 4 in Excel
We’ve updated sea ice, so it’s time to have another look at the global temperature series I last updated nearly a year ago. Here’s the instrumental averages with another year on the traces. Ho-hum … still shooting up; still on track
. . . → Read More: More paint drying
Here are all those charts updated and collated for you… . . . → Read More: Season 2014 roundup