Thursday, September 3, 2015

Global surface August - NCEP/NCAR index up 0.14°C

Here are the results for the Moyhu NCEP/NCAR reanalysis index for August. It looked for a while to be a record breaking month, with steady warmth. But then there was a sudden late cool spell, apparently mostly Antarctica, and then an equally sudden recovery to warmth. The end result was 0.306°C, a big rise from July's 0.164. That is just slightly cooler (in this record) than May 2014 at 0.315°C. But it is the warmest for 2015 so far.

It makes a slight difference month-month what anomaly base period is used, and so the Moyhu table gives results also on the 1951-80 base (gor GISS) and 1961-90 (NOAA Mlost). So the comparable GISS-base number would be 0.87°C. But as mentioned in earlier posts, the NCEP index, being air temperature, has been running rather cool relative to the land/ocean indices which using the warm current SST. So I would not be surprised if GISS were even higher - maybe even 0.9°C. The record GISS anomaly is Jan 2007 at 0.96°C.

ps This post is a little later than usual. The volatility meant that I wanted to go right to the end of month, and the last day of NCEP/NCAR was posted a little late at NOAA.

Wednesday, September 2, 2015

Sea ice melt

Neven has the full story, but the Arctic ice extent is still reducing; area not so much. The JAXA SIE has 2015 now below 2007, but still well above 2012. Whether it will finish below the 2007 minimum is unclear, since 2007 continued melting for a long time. But either second place or third looks likely.

In the same table, NSIDC has 2015 fourth, but just behind and likely to overtake 2011, which did not melt much in September.

In the Antarctic, NSIDC SIE had been briefly rising, but now some melt again. It's below all years since 2008.

Sunday, August 30, 2015

Daily reanalysis variation - smoothed maps

I have been posting updated daily NCEP/NCAR reanalysis based indices of global temperature. They fluctuate a lot, but give a reasonable estimate of monthly average. For example, in August 2015, after a period of warmth there has been a sudden dive. I also post daily maps, so it is natural to look at these to see what is causing this. Since weather effects take some days to evolve on a global scale, it is unlikely that the sudden changes are coordinated worldwide. They are likely to be locally identifiable.

The current maps, however, make this difficult because of the detailed patchwork of colors. Color mapping for daily data presents a dilemma, because of the high variation over land compared with sea. I thought I had an optimal solution, where the scale ensures that the 256 colors in my rainbow map occur equally often. From an information viewpoint, this makes the best use of our color discrimination capability. But the area is dominated by sea, where a lot of colors are used to resolve a fairly narrow range. That leaves only a few colors to represent the extremes on land, where the colors lurch from red to blue with little in between. But devoting more colors to the land range makes the oceans bland, and in particular ENSO patterns disappear. They are large relative to monthly average spatial variation, but not wrt daily.

I shifted the color scheme toward better land resolution, compromising with ENSO and ocean. But what I really want to talk about is a new scheme using spherical harmonic (SH) smoothing. This enables you to clear away as much as you like of the detail, and show broad swathes of warm/cool. It doesn't show the extremes of variation, but gives a better idea of how much the various regions contribute to the whole integral.

There is quite a lot of work behind the scenes, which I may describe in future. The SH is worked out in Javascript, so you can choose the level of smoothing. The WebGL plot, similar to that on the data page, has a selection box, where you can choose numbers from 3 to 13. The numbers represent approximately the spatial frequency - ie inverse of resolution. Choosing 3 means the characteristic wavelength is 1/3 the circumference etc. There is also the choice of Full, which is not smoothed, but the data shaded directly.

I'm planning to add this to the main page, which will take some data processing. The current model just shows one day, 27 August, when that cold spell took hold. The plot, with some explanation, is below the fold.

Friday, August 21, 2015

NOAA says July hottest month ever

here but I wish they wouldn't. In fact the anomaly was down from 0.87°C in June to 0.81°C. Oddly, that change is exactly what TempLS mesh now shows, while TempLS grid has the same drop (0.04°C) as GISS. Usually it is the other way around.

OK, so it is a warm year, and this was still the warmest July on record. The NOAA claim that it is the warmest month ever (also Tamino) is based on the annual cycle of absolute temperature, whereby ocean-cominated SH summers are cooler than NH, with less seasonal variation.

Why is this a silly point? The NOAA has a sensible discussion here on the reason for using anomalies in preference to absolute - see point 7. Yet they don't seem to be able to stick to it. They keep lapsing into quoting an annual absolute global temperature, and of course regularly quote a ConUS absolute average.

And it just gets them into trouble, pointlessly. The global absolute is got by adding the anomaly to an annual climatology (14°C) taken from a Phil Jones 1999 paper. But the average anomaly is known rather well, the climatology very poorly in comparison. So the sum is worth far less than the parts. Every now and then, a  sceptic raises the 1997 estimate of 62.45°F (16.92°C) for that year and says - see! the world has cooled over 2°C since. NOAA has been forced to add a feeble disclaimer to the 1997 report. But the sceptics are right (for once) to point this out. It just makes the NOAA look dumb. And of course the troubles caused with the absolute average for ConUS (in clumsy hands) are innumerable.

Back to July - we knew that the global absolute has that seasonal  cycle. It doesn't mean anything in terms of climate change, and isn't news. March had a very high anomaly, July less. But July will always exceed March in absolute.

That's one of the main things about anomaly - it contains the news. The information content about weather and climate change. If I tell you that it was 17°C here yesterday, you won't be impressed. The natural question is - what is it normally? Ie, what is the anomaly? And then you find that it is indeed quite warm for an August day.

You can see this news issue in a temperature map. If you see an absolute temperature map for July 2015, it looks like any other July. Sure, it tells you that Melbourne Fl is warmer than Melbourne Australia, and much other climatological information. But it doesn't tell you much about July 2015. For that you need the anomaly map.

NOAA knows all this. I just wish they would stick to it.

ps In other news, August so far is pretty warm. And Arctic Ice is still melting, with 2015 chasing 2011 for third place. Antarctic ice has entered a freezing pause, which may be linked to ENSO.





Wednesday, August 19, 2015

USHCN adjustments - a case study

In my last post, I linked to a post at Steve Goddard's, grumbling about my comments at WUWT, where I linked to my earlier post showing a breakdown of total adjustment by states. A commenter picked up on New Hampshire, saying
"I particularly like nick’s new hampshire graph with a whole degree warming suddenly applied a few years ago. Their actual temps were obviously not cooperating."

Well, there is indeed a steep rise at about 1991:



The new graph shows that in 1991-2 there was a total rise of about 1°F made up about equally of a TOBS rise and a non-TOBS part. How could that happen? I investigated.

Tuesday, August 18, 2015

USHCN again - adjustments breakdown


There is another post at WUWT based on Steven Goddard's discredited plots of USHCN adjustments. I dealt with that in detail here. What Goddard does is to calculate the effect of adjustment by taking the difference of one set of stations, adjusted, and a different set (a subset) unadjusted. But of course, the difference includes the climatic differences between the disparate station sets, which are not a result of adjustment. And I show there that that dominates, by simply repeating the calc with long term means replacing the monthly data for the non-overlapping part. The result is very similar, showing that the difference is not due to adjustment, or even weather, but to the different climate mix of the stations.

Prof RG Brown was the promoter of the SG graph at WUWT, but he didn't seem to have much interest in where it came from. I think there is still no link to the Goddard source article (which tells nothing anyway). But I can use one of his analogies to describe why the SG approach is all wrong. RGB postulates a process where the growth of young trees is quantified by measuring total height with a tape measure. So, suppose you measured 5 trees with a tape, and 5 other trees with a ruler. You suspect that the tape may be biased high. So you subtract the mean of the two sets of 5, and say that is the difference due to the tape vs ruler.

But of course it isn't. There is no control to say that the tree heights match. The average heights would be different even if all measured with the same measure. OK, in the USHCN the raw stations were a subset of the adjusted, so a closer analogy would be having all 10 measured with tape, and a subset of 5 with ruler. You still can't quantify the ruler/tape by just differencing means of 10 and 5.

Anyway, SG posted a response here. It is worthless, because he won't let go of the nonsense calc. He disputes my observation that TOBS is a major part by showing two graphs, both with his nonsense component. Yes, of course that is then indeed dominant. Most ridiculously, he says:
"Nick also claims that I am comparing two different sets of stations. This is complete BS. USHCN fabricates missing data for almost half their stations. That is an utterly unacceptable practice."
That is a complete non-sequitur. The stations are different places. Whether he thinks they shouldn't be is irrelevant.

A while ago, to counter some other clumsy mis-calcs of USHCN, I posted a breakdown of USHCN adjustments by state. I'll repeat this below the fold showing separately, as NOAA does in its data, the part due to TOBS, the other adjustment (mainly homogenisation), and the total. TOBS is the biggest, and it is the part with a consistent uptrend, for well-established reasons set out here.

Meanwhile, over a year ago, NOAA rolled out its new nClimDiv system, which really makes all this obsolete.

Saturday, August 15, 2015

GISS down by 0.04°C in July

The GISS global anomaly average fell from 0.79°C to 0.75°C in July. This is quite close (as expected) to the decrease (now 0.059°C) in TempLS mesh. It is even closer to the 0.039°C drop in TempLS grid.

It is also the same as the 0.04°C drop in the NCEP/NCAR average. Early in August, I said
"NCEP July was fairly close to April, so those are a reasonable guess for July - ie GISS 0.74°C, NOAA 0.78°C. But I wouldn't be surprised to see them a little higher."
Meanwhile GISS June was adjusted down by 0.01.

The spatial pattern is quite similar to that of TempLS and the NCEP/NCAR based average. Maps below the fold.