There has been a lot of interest in USHCN adjustments. Paul Homewood has been tabulating data from various states, most recently Ohio. Steven Goddard has been getting publicity with various flaky graphs. In criticising one of these, I posted a plot of average US adjustments. In doing so, I followed SG's practice of a simple average across the USA. It would be better to use some kind of area weighting.
Zeke Hausfather has been writing a series at Lucia's, and there have been various posts at WUWT.
So I thought it would be useful to post a complete series of plots of the effects of USHCN adjustments on the individual states, and then an average of these weighted by state area. This should give similar results to gridding. So there is an active plot below the jump. Note that the results are in °F, which seems to be traditional for USHCN.
So here is the plot. It initially shows the area-weighted US average of USHCN annual final - raw (not including 2014). Below the plot, you'll see a table of state initials. Click on any of these to bring up that state.
Small states can be quite ragged. The larger states generally have rather smaller adjustments, which is why the area weighted adjustment variation is less, and also less than the simple average.
As to why the general trend is upward, I have a series of posts - you could start here. The big one is TOBS (Time of Observation adjustment), which compensates for a drift due to a shift from late to early theromoeter resetting. It made an artificial downtrend, so the correction trends up.
Update. The code is here. Steps are
Loop over states{
Collect state raw and adjusted
Loop over stations{
Place differences in month x year array (NA if not both raw and final
Average over months each year
}
Average over stations each year
Add to array year x state
Plot
}
Get area weighted average over states and plot
Posts
Thanks Nick. Wish you had kept all of the graphs on the same scale though.
ReplyDeleteThanks, Carrick,
DeleteThe downside of same scale is that it uses most of the y-axis to cover fluctuations in small states which don't contribute much to national average. But the other deterrent is that for comparability they should really all be set to consistent levels. It's a puzzle to me that they don't consistently show present as near zero, although the national average is close to zero there. Version 2 was supposed to do that.
I suspect the reason is all the fussing about the "past constantly changing". If they fully enforced present adjusted=raw, then indeed the past would be constantly changing.
How about same scale, different offset?
DeleteOK, Current plan is to produce an active spaghetti plot. For that I am inclined to align them to 2013 zero.
DeleteThat sounds great! Thanks for your efforts.
DeleteDrift is at best an inelegant way to describe TOB as you are not making the measurement under the same conditions. The temperature is changed. The temperature today at Noon and the Temperature at midnight, or any other 12-hour separation, will very likely be different and it is not instrument drift but a real change.
ReplyDeleteNo explanation on why the adjustments generally increase post 1936?
The only change to the past should be TOB and only once. Constantly adjusting the past, then referring to differences with the past, is not particularly sound.
"No explanation on why the adjustments generally increase post 1936? "
DeleteI think the answer may be television. No, seriously. Originally the NWS asked observers to read and reset late afternoons, and I think this may have been so the readings were available for the morning newspapers. But the same observers were often reading the rain gauges in the morning. When people started expecting to hear about weather on the evenong news instead of the morning paper, that rationale lessened, and volunteers could change to read the gauges and temp at the same time.
OK, speculation. But the data is here. Reading times did start shifting from pm to am starting around 1940.
Visually comparing the USA graph to your May 9th graph shows that there is little difference between state area and simple average across the USA methods. -- John M Reynolds
ReplyDeleteHow is this actually any different at all from what Steve Goddard has been publishing ?
ReplyDelete"Steven Goddard has been getting publicity with various flaky graphs"
Haven't you just confirmed in efffect what he has been saying all along ?
http://stevengoddard.wordpress.com/tracking-us-temperature-fraud/
Forget his political stance. Nearly every single graph he has published, matches yours.
Highest Adjustments in the 1930/40s.... to "Remove" the inconvenient warm spike... less upto the present day.
The graphs Steve Goddard has produced, match this post almost identically. Across the board.
The graphs are flaky. Remember the spike? That was a failure of methodology. And he corrected only part of it. His plots show the sum of adjustment and another part that reflects differences between the climates in places that did and didn't report. With luck, that last part will cancel out, and it often does, almost. But you can't rely on it.
DeleteSorry, Nick. We don't see that. And we out here don't give a damn about your fights with Goddard/Heller.
DeleteWe care that there are people who are going to be dead next Xmas--the homeless--because you climate guys can't tell the G.D. truth. And that's what it looks like to the ignoramuses out here.
Tell me exactly how your graph above differs from Goddard/Heller's.
So you can't see the difference between this one and this one? Check the axes.
DeleteAnd why do you think area averaging helps here. Nobody is trying to create a temperature reconstruction at this point. Just trying to show how the best approximation to primary temp data is modified before averaging. Area averaging just puts more fudge factors and wiggle room into equation.
ReplyDeleteThe published US results are area averaged. Otherwise, they would overly reflect temps in densely settled parts. I'm trying to show what contribution adjusting makes to that average.
DeleteThanks Nick.
ReplyDelete