Sunday, July 20, 2014

Trends of gridded BEST and GISS shown with WebGL


This is a next stage in the display of Earth data with WebGL. It uses the general framework described here. BEST and GISS are temperature anomaly datasets, available on a 2° by 2° grid. In fact, BEST has 1° resolution, but I amalgamated cells to match GISS, mainly to alleviate download time. GISS uses ERSST ocean data.

For each cell, an OLS trend coefficient over time (°C/century) is calculated and shown with color shading. You can choose start and end years, from 1889 to 2013. Press "Plot New" when you have made a choice. The Earth is a trackball, as in Google Earth. You can press "Orient" to get it right way up.

When you click on any point, the location and trend are shown on the right.

BEST describe their methodology in various papers, access from here. GISS methods are described here, with links.


Update. I've implemented Carrick's suggestions - see below plot for details.

Tuesday, July 15, 2014

June GISS Temp down by 0.14°C

GISS has posted its June estimate for global temperature anomaly (h/t JCH). It fell from 0.76°C in May to 0.62°C in June. TempLS declined, but only slightly.

China data for May is now in. It did not change the GISS number.

The comparison maps are below the jump.

Sunday, July 13, 2014

TOBS pictured

This is a version of my TOBS nailed post, with graphics. The numbers come from my first post in the series, which took three years of hourly data from Boulder, Colorado, and looked at the effect of TOBS (time of observation) measures. That post is the place to look for detail on how it works. A post with much more data is here.

For now, I want to follow the recent post in relating TOBS to fundamentals. What is our measure of average temperature over a period? Sometimes people strenuously urge that the usual TAVG, the average of daily recorded min and max, should be replaced by a proper integral over the day. And they would be right, if we had the historic data. But we don't.

What we do have are records of min and max as recorded daily (at various times of day) by min/max thermometers. These give not the actual daily min/max, but the min/max in the preceding 24 hours (with regular resetting). So they are, averaged over time, a reasonable measure of average temperature, but a measure that depends on the time of observation.

Let me show that with a plot of the three years of Boulder data. I have taken the mean of the hourly data, and compared with the measure that a notional observer would report from reading a min/max every day at at 2AM, or at 5AM and so until 11 pm. I show the 365 day centered running mean that you would get by each of these schemes. The running mean removes the seasonal cycle. The legend shows the colors, with a link to the respective curves. Left axis °F, right in °C. x-axis days after 31 Dec 2008.



So the various TAVG curves are reasonable measures, in that they track the black mean curve with a roughly constant offset. But the offsets are very dependent on time of obs.

If you stick with one such measure, the offset does not matter much. Its effect would go away on taking anomalies. But if you switch between measures (change TOBS), the effect can be large.

TOBS adjustment is effectively calibrating this measure, relative to a reference. If you change measures, you have to recalibrate.

When we refer to "raw" or "unadjusted" monthly data, it should be remembered that it is not just the original readings. It incorporates an averaging procedure. The outcome of that depends on the time of observation. If that changes, then it's a different measure, as much as if you changed to a differently calibrated thermometer.

Below the fold, I'll show some plots of monthly averages, and a difference plot that may make the stability of the TOBS dependence clearer.

Friday, July 11, 2014

TempLS global temp down 0.015°C in June

TempLS global land/ocean anomaly dipped very slightly in June; from 0.605°C to
0.59°C. That's still high. The May reading was slightly boosted by the late arriving China data. GISS is steady at 0.76°C, but I don't know if they have used the new data.

Again there are many (95) errors in the GHCN unadjusted file, detected by my new program which compares adjusted and unadjusted. No especially huge ones; Port Hardy for example is still getting its data from the frozen North, but even that is warming up a bit. The big problem area is Turkey, which seems to have an OK CLIMAT form, but GHCN has entered the April data. Still problems with Greenland.

Wednesday, July 9, 2014

Someone is wrong on the internet

again. More bad averaging and USHCN. This time, it arises following a very good post by Zeke Hausfather on USHCN adjustments. He showed this plot of the effect of infilling. It isn't much.

A blogger and commenter there, sunshinehours1, said, no, that's misleading information. And he shows how the average of estimated final less rises faster than the average of non-estimated.

It has been reblogged by Paul Homewood, and looks like it is getting around. But it's the same bungled methodology that Steven Goddard used. The stations in that average, plotted over the years, change substantially from year to year. They could be just be an increasing number of warmer stations. Since climate differences are large, it doesn't need a big imbalance to show up.

So the same refutation will work here. Simply work out the difference using just the climatology of the stations. No use of estimation, or indeed annual data. And you get the same result. It isn't telling you anything about the effect of estimation. It is just telling you about the changing nature of the stations being estimated.

Tuesday, July 8, 2014

GHCN Adjustments are much larger in US than ROW.


ROW=Rest of World. I wrote two posts here and here on the overall effect of global GHCN adjustments on trends of various periods. It was a kind of sequel to my first ever Moyhu post here, which showed a similar histogram of the effect of V2 adjustments.

I was a little surprised that the positive bias had increased substantially, though still not huge. There has recently been a lot of talk about USHCN adjustments, and I did some plotting in my most recent post. GHCN v3 just uses USHCN, including the adjustment method, for its US data. So I thought the rise might well be partly due to the US component and TOBS.

It turns out that is true. I repeated the calc separating US and non-US data. I'm using just a simple average of the effects on trends - it should be area weighted. That's part of the reason why the US has a disproportionate effect in this simple average. Anyway, the trend differences caused by adjustment to stations with 60 years data were 0.0355 °C/decade for US, 0.0248 °C/decade for ROW, and 0.0284 °C/decade combined. More numbers and details below.

Incidentally, that previous post had a useful Google Maps gadget that lets you pick out groups of stations with big or small adjustments, rural/urban etc. I'll probably update it with more links and more intuitive logic.

Sunday, July 6, 2014

USHCN adjustments plotted for USA and States


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.