Saturday, April 28, 2012

Interactive JS plotter data list

Some more news and information about the Moyhu interactive plotter. The main content of the post is a listing of the data, with links. But first some discussion of versions and some updates on progress.

I have reinstated in V2 the ability to copy a URL which will reinstate the plot as you see it. This is intended for linking. There are minor limitations - it won't pass on user data, and it won't regenerate vectors made with the Calc box.

This got me thinking about version stability. If people are going to store links, I need to ensure that the code will still work if I add more data, for example. So my plan now is this. Versions will appear in blog posts, and I'll then leave them alone except for bug fixes. If you copy a link from such a site, it will refer back to that post, and should work indefinitely.

I'll keep the latest version of the plotter on a page. You can see the pages listed top right. I might add data there, and not post a new version for a while. It will allow you to copy URL's which will refer to that page but I won't guarantee that they will work in exactly the same way long into the future. For that, you should go to the most recent blog post version.

Data listing

The general way to get documentation in the plotter is to Ctrl-Click on any active button. That is, click with the Ctrl key depressed. On the Mac, I believe the Command key has similar effect. The information appears bottom right (green window) or on a separate tab if you have checked the New Window button.

If the button connects with data, you'll get info about the data. The buttons next to the selection boxes give a general description for the datasets contained. If you have plotted a set, there will be an entry in the plot list with a checkbox to it's right. If you Ctrl-Click that, the info window (bottom right) will show specific info, usually a link to source and another to an info page on the web.

Below is a listing of that information for each of the data sets that you can plot. The red headings below are the names of the selection boxes - then follow the contents.

1HADCRUT 3HADCRUT 3 Global Land and Ocean Index Source More information
2HADCRUT 4 GlobeHADCRUT 4 Globe Source More Info
3HADCRUT 4 NHHADCRUT 4 NH Source More Info
4HADCRUT 4 SHHADCRUT 4 SH Source More Info
5HADCRUT 4 TropHADCRUT 4 Tropical Source More Info
6Had 4 NH Nov-FebHADCRUT 4 NH Nov-Feb Source More Info
7Had 4 NH Mar-MayHADCRUT 4 NH Mar-May Source More Info
8Had 4 NH Jun-AugHADCRUT 4 NH Jun-Aug Source More Info
9Had 4 NH Sep-NovHADCRUT 4 NH Sep=Nov Source More Info
10Had 4 SH Nov-FebHADCRUT 4 SH Nov-Feb Source More Info
11Had 4 SH Mar-MayHADCRUT 4 SH Mar-May Source More Info
12Had 4 SH Jun-AugHADCRUT 4 SH Jun-Aug Source More Info
13Had 4 SH Sep-NovHADCRUT 4 SH Sep-Nov Source More Info
14CRUTEM3CRUTEM 3 Global Land Only Source More information
15HAD NHHADCRUT3 NH mean Source More information
16HAD SHHADCRUT3 SH mean Source More information

17GISSGISS Global Land and Ocean Source More information
18GISS TsGISS Met Stations (Land Only) Source More information
19ConUSAnnual Temp Anomaly Lower 48 USA Source More information
20ArcticAnnual Temp Anomaly Arctic 64-90°N Source More information
21NH TempAnnual Temp Anomaly NH Temp 24-64°N Source More information
22TropicAnnual Temp Anomaly Equator -24-24°N Source More information
23SH TempAnnual Temp Anomaly SH Temp 24-64°S Source More information
24AntarcticAnnual Temp Anomaly Antarctic 64-90°S Source More information

25NOAA GlobNOAA Glob Land/Ocean Source More information
26NOAA NH Land/OceanNOAA NH Land/Ocean Source More information
27NOAA SH Land/OceanNOAA SH Land/Ocean Source More information
28NOAA Glob LandNOAA Glob Land Source More information
29NOAA NH LandNOAA NH Land Source More information
30NOAA SH LandNOAA SH Land Source More information

Satellite Temp
31UAHUAH Lower Troposphere Source
32MSU-RSSMSU-RSS Lower Troposphere Source More Info
33UAH GlobeUAH Globe Source More Info
34UAH All LandUAH All Land Source More Info
35UAH All OceanUAH All Ocean Source More Info
36UAH NH AllUAH NH All Source More Info
37UAH NH LandUAH NH Land Source More Info
38UAH NH OceanUAH NH Ocean Source More Info
39UAH SH AllUAH SH All Source More Info
40UAH SH LandUAH SH Land Source More Info
41UAH SH OceanUAH SH Ocean Source More Info
42UAH TropicsUAH Tropics Source More Info
43UAH TropicLandUAH TropicLand Source More Info
44UAH TropicOceanUAH TropicOcean Source More Info
45UAH NH ExtraTropUAH NH ExtraTrop Source More Info
46UAH NHExtr LandUAH NHExtr Land Source More Info
47UAH NHExtr OceanUAH NHExtr Ocean Source More Info
48UAH SH ExtraTropUAH SH ExtraTrop Source More Info
49UAH SHExtr LandUAH SHExtr Land Source More Info
50UAH SHExtr OceanUAH SHExtr Ocean Source More Info
51UAH NPolar AllUAH NPolar All Source More Info
52UAH NPolar LandUAH NPolar Land Source More Info
53UAH NPolar OceanUAH NPolar Ocean Source More Info
54UAH SPolar AllUAH SPolar All Source More Info
55UAH SPolar LandUAH SPolar Land Source More Info
56UAH SPolar OceanUAH SPolar Ocean Source More Info
57UAH USA48UAH USA48 Source More Info

Misc Temp
58BESTBEST Land Only Source More information
59TOS ModelSource More information
60Foster/Rahm GISSFoster&Rahmstorf GISS land/ocean modified for exogenous effects Source More information
61Foster/Rahm NCDCFoster&Rahmstorf NCDC land/ocean modified for exogenous effects Source More information
62Foster/Rahm CRUFoster&Rahmstorf CRU land/ocean modified for exogenous effects Source More information
63Foster/Rahm RSSFoster&Rahmstorf RSS land/ocean modified for exogenous effects Source More information
64Foster/Rahm UAHFoster&Rahmstorf UAH land/ocean modified for exogenous effects Source More information

65HADSST3HADSST3 Global Sea Surface Temperature Source More information
66HADSST2HADSST2 Global Sea Surface Temperature Source More information
67HADiSSTHADISST Global Sea Ice and Sea Surface Temperature Source More information
68NOAA Glob OceanNOAA Glob Ocean Source More information
69NOAA NH OceanNOAA NH Ocean Source More information
70NOAA SH OceanNOAA SH Ocean Source More information
71OHC 0-700mGlobal Ocean Heat Content 0-700m 10^22J Source More Information
72UEA SOISouthern Oscillation Index (UEA) Source More Info
73AMOAMO Reconstruction (Mann 2009b) Source More Information
74PDOPDO Reconstruction (Mann 2009b) Source More Information
75JISAO AAOSouthern Oscillation Index (JISAO) Source More Info
76JISAO NAMNorthern Annular Mode (JISAO) Source More Info
77JISAO ENSOENSO (JISAO) Note that ENSO has short term variation and annual averaging smooths it a lot. Some peaks are lost. Source More Info

Giss Forcings
78AllGISS Model E forcings Global mean net Source More information
79W-M GHGGISS Model E forcings Well-Mixed GG Source More information
80OzoneGISS Model E forcings Ozone Source More information
81StratH2OGISS Model E forcings Stratospheric water Source More information
82SolarGISS Model E forcings Solar Irradiance Source More information
83LandUseGISS Model E forcings Land Use Source More information
84SnowAlbGISS Model E forcings Snow Albedo Source More information
85StratAmerGISS Model E forcings Stratospheric Aerosols Source More information
86BCGISS Model E forcings Black Carbon Source More information
87Refl AreGISS Model E forcings Reflectic Trop Aerosols Source More information
88AIEGISS Model E forcings Aerosol Indirect Effect Source More information

89MLO CO2Mauna Loa CO2 Source More Information
90C13/C12 MLOMauna Loa δC13 Source More Info
91C13 SignatureGlobal Stable Carbon Isotopic Signature Source More Information
92GISS CO2GISS CO2 Source More Info
93Nitrous OxideNitrous Oxide Source More Info
94MethaneMethane Source More Info
95TSI ReconTSI Reconstruction Dora Source More Information
96SIDC SunspotsSIDC Yearly Sunspot Number Source More Info
97FF emissionsTotal carbon emissions from fossil fuels million metric tons of C Source More Info
98FF Gascarbon emissions from gas fuel consumption Source More Info
99FF Liquidcarbon emissions from liquid fuel consumption Source More Info
100FF Solidcarbon emissions from solid fuel consumption Source More Info
101From Cementcarbon emissions from cement production Source More Info
102Flaringcarbon emissions from gas flaring Source More Info
103FF Per CapPer capita carbon emissions metric tons of carbon after 1949 only Source More Info

104TempLSTempLS Global Land and Ocean Source
105TempLS GHCN 3 HSST2TempLS GHCN 3 with HadSST2 More Info
106TempLS CRUT 3 HSST2TempLS CRUTEM 3 with HadSST2 More Info
107TempLS GHCN 3TempLS GHCN 3 Land Only More Info
108TempLS CRUT 3TempLS CRUTEM 3 Land Only More Info
109TempLS GHCN 3 HAD3TempLS GHCN 3 with HADSST3 More Info
110TempLS CRUT 3 HSST3TempLS CRUTEM 3 with HadSST3 More Info

111NCEP-PWPrecipitable Water Source More Information
112OLROutgoing Longwave Radiation Source More Information
113NCEP-MEINCEP Multivariate ENSO Index Note that ENSO has short term variation and annual averaging smooths it a lot. Source More Info
114NCEP-NAONCEP Nprth Atlantic Oscillation Source More Info
115NCEP-NINO3NCEP East Central Tropical Pacific SST Source
116NCEP-ONINCEP Oceanic Nino Index Source
117NCEP-SOINCEP Southern Oscillation Index Source More Info Units, 10*t-stat
118Surf SHSpecific Humidity 1000Mb Source More Information
119NCEP-SH850Specific Humidity 850Mb Source More Information
120NCEP-SH700Specific Humidity 700Mb Source More Information
121NCEP-SH600Specific Humidity 600Mb Source More Information
122NCEP-SH500Specific Humidity 500Mb Source More Information
123NCEP-SH400Specific Humidity 400Mb Source More Information
124NCEP-SH300Specific Humidity 300Mb Source More Information

Thursday, April 19, 2012

Interactive JS plotter Ver 2

Time for the next version of the interactive Javascript plotter. A few new capabilities, and some limitations removed.

The major new things are:
  • A new layout. The data now sits in selection boxes, so I can include mich more of it. It does mean two clicks instead of one.
  • A new multiple regression capability, which I think is quite powerful. You can use any of a number of calculated functions, or any data vectors as regressors. For example you could regress temperatures against various forcings.
  • To complement this, a calculation capability - you can create and plot linear combinations (eg differences) of data vectors.
  • An image can be used as background. This is useful for superimposing plots on published graphs (as in the Hansen predictions post).
  • A user input capability. You can input your own images, or your own data.
  • New windows for the markers for the plotted curves and their axes. This removes the limitation on how many you can have.
  • More smoothing options
One thing that doesn't currently work is the ability to provide a web address that would regenerate the current state. This is because of the greater number of state variables. But I'm working on it. Update: it works. See new post.

The new data consists mainly of more complete collections of things like the regional data of GISS and UAH, and more NCEP data. And HADCRUT 4 is included. But there's room for more - suggestions welcome.

Here's the plotter - details below.

Monday, April 16, 2012

GISS Temp for March 2012 - comparison

TempLS showed a rise in global mean anomaly in March, 2012, from 0.19 °C to 0.293 °C. GISS showed a somewhat smaller rise, from 0.4 °C to 0.46 °C. The satellite indices showed rises larger than TempLS. Time series graphs are shown here

As usual, I compared the previously posted TempLS distribution to the GISS plot. Here is GISS:

And here is the previous TempLS spherical harmonics plot:

There is a disagreement over the Phillipines, where I get a cold spot and GISS doesn't. Otherwise they match well.

Previous Months


More data and plots

Saturday, April 14, 2012

US temperature trends.

These are being discussed again. WUWT has rediscovered the NOAA analysis of USHCN adjustments to ConUS (lower 48 states) temperatures. They have inflated the original Fahrenheit adjustment trends restated in Celsius to make their argument. It's accpmpanied by a recent analysis by Roy Spencer of ISH data, which he has assembled into an index for ConUS, and finds that the trend 1973-present is small. But his figure includes his own adjustment for population density, and comes out to 0.013°C/decade. This he compares to USHCN at 0.245°C/decade, and attributes the difference to USHCN adjustment. CRUtem 3, which doesn't make the same adjustments, gets a trend of 0.198°C/decade.

It's unfortunate that Roy doesn't seem to give any trends from ISH that aren't subject to his population density adjustment, because then we could compare like with like. However, I will look at the GHCN unadjusted data for ConUS. This is a real test of whether the WUWT (and RS) claim that the trend is due entirely to adjustment is justified. It isn't much of a test if you strike their adjustments and then introduce a big new one.

I actually looked at ConUS with an early version of TempLS here. I looked at the period 1978-2009, and got a trend of 0.255°C/decade. This was using GHCN v2 min/max mean monthly data, with weighted regression using 5x5° cells. I thought I would update this calc, and for the period used by Roy. With TempLS V2.2, GHCN V3 V2 data and basically the same weighting, but with a period 1973-2011, I got a trend of 0.161 °C/decade. A bit less than CRUTem 3, but clearly there is trend in the unadjusted data too.

Along the way, I discovered a trick question. What is the easternmost US GHCN Met station?

The answer is Shemya AFB in the Aleutians, Longitude 174° East. That did trick me - in TempLS you provide logical statements to specify subsets, and I thought for ConUS requiring country code 425 (US) and east of Long 125W would do it. So Shemya, and another station at Attu, got included. There weren't many readings, and I don't think they changed the trend by much.I've fixed it now.

Station distribution bias

Anyway at WUWT Willis Eschenbach asked what precautions I had taken to avoid E coast bias. Probably the first thing to say is that GHCN has taken precautions. They have been criticised for pruning their list of stations but one thing that has been achieved is a reasonable uniformity. Here's a map of the stations reporting in 2011, and also all stations reporting at some point since 1972:

The distribution isn't that uneven. However, Ver 2.2 has some capabilities in mesh-based weighting. I was experimenting with those in Antarctica, and so I saw a chance to try them out here.


The first thing I should say is that I haven't generally found that messing with weighting schemes changes average trends a lot. I should emphasise that the weighting scheme doesn't change the data, only the way it is included. Reweighting one station relative to another only has a big effect if they are very different. And of course, the changes tend to balance out in the average.

The purpose of weighting is essentially to form a sum which is a good approximation to a space integral. This has the appropriate physical significance, and also gives a rational basis for saying that bias due to clustering of stations has been overcome.

The best way I have found to use meshes is to surround each node of the mesh (which is done for every month, so the nodes are the stations reporting in the month) by the area formed from the adjacent portions of the triangles marked out by the medians and the edge bisectors. The preferred method is Voronoi, which forms the in-centres and the perpendicular bisectors. But here we're only trying to associate with each node an area for weighting purposes, so it isn't critical to have the areas exactly demarcated by closest neighborhood, as long as they are close and unique. And this scheme is a lot faster, which is important when you need a new tesselation every month over 40 years.


I generate the meshes using R's convhull function. I use the 3D version, which means that I find the hull of the curved area (US). There is a back mesh, which is fairly easy to get rid of because the normals face the other way. But for a country shape, it incorporates bays etc with the land area.

For Antarctica I had trouble with the Weddell Sea and used a scheme for carving part of it out. This time I tried a new scheme which gets rid of the back mesh as well. I introduce a ring of new points (before meshing) which form a ring outside the stations. The idea is that if you ran a string around the surrounding nodes, it would be clear of any real stations. Then after having meshed the larger area, I eliminate the extra nodes and any triangles entailed. That tends to strip down to the real country outline. It's worth reminding that TempLS doesn't actually know about borders - it only has the location of the selected stations.

Meshes and weights

So here is the result. I'm showing the mesh in the background and weights proportional to the area of the circles around each station reporting in a particular month.

You can see that stations in sparser areas are upweighted according to the area they are taken to represent.


With the most recent mesh as shown in the diagram, I found that the trend using unadjusted GHCN monthly data for ConUS stations was 0.167°C/decade from 1973 to 2011. This is very similar to the cell-weighted figure (0.161), somewhat less than CRUTEM 3 (0.198) and less again than USHCN (0.245). Notwithstanding, I believe that the USHCN adjustments are justified, and theirs is the preferred figure. But is certainly isn't true that the adjustments are responsible for the uptrend.

More on adjustments

At WUWT Geoff Sherrington made the predictable objection that GHCN unadjusted data has been tampered with by the met stations. I've seen this often, but never with evidence.

In fact the original GHCN (mid 90's) was compiled direct from the records, to the extent that they preserved the various fragments from different sources. They then distributed that on CD, to many recipients.

In more recent times, I have never seen any indication that GHCN unadjusted records have been adjusted. There are occasional records removed when errors are found. Others come in late and are included. But they say that they don't modify records except on receipt of an amended CLIMat form from the Met supplier, and that seems to be true. Since the mid 90's these forms have been used exclusively, and you can inspect them here.

Wednesday, April 11, 2012

March 2012 temperatures up 0.1°C

The TempLS analysis, based on GHCNV3 land temperatures and the ERSST sea temps, showed a monthly average of 0.29°C, up from 0.19 °C in February. This is in line with satellite LT trends, though a more modest rise. There are more details at the latest temperature data page.

Below is the graph (lat/lon) of temperature distribution for March. The big US hotspot is very clear

This is done with the GISS colors and temperature intervals, and as usual I'll post a comparison when GISS comes out.

And here, from the data page, is the plot of the major indices for the last four months: