Bruiser Murray writes:
Several times I have commented that the BOM has falsified Solar Exposure data for 2013. Recently “Griff” challenged me to substantiate the claim. I have not had access to my old computer where some of the files were stored so, until recently, could not provide the evidence. That changed last week and the attached file provides the basis if my assertion.
1. On several occasions, information has been provided to your blog that the Australian Bureau of Meteorology (BOM) has comprehensively revised solar exposure data recorded in 2013. This was done so that the revised data would support the BOM assertion that 2013 was the hottest year on record due to anthropogenic drivers.
Background
2. In Jan 2014, the alarmist web site “Sceptical Science” ran an article: AUSTRALIAS HOTTEST YEAR – HUMANS CAUSED IT. The complete article and comments can be viewed here:
https://www.skepticalscience.com/australias-hottest-year-humans-caused-it.html
Selected excerpts are included below. After reading the article, I (Bruiser) posted the following comments:
bruiser at 20:16 PM on 20 January, 2014
2013 is a poor example to choose as the poster child for AGW. In fact, quite the opposite. Taking Sydney as an example, new records for solar radiation were set for May, July, August, September, October and December. The average monthly maximum temperatures track the solar radiation trends. The really hot days seem to correlate closest to low humidity; suggesting that the absence of H2O allows transmission of more solar radiation. All three climate systems, IOD, ENSO and SAM favour dryer conditions across Australia.
The comment resulted in several individuals responding, with one posting the BOM solar exposure chart for 2013.
barry at 21:37 PM on 20 January, 2014
bruiser @ 13,
Eyeballing the BOM average and anomaly maps, the amount of solar exposure for NSW over 2013 was about average.
Are you able to furnish data links to corroborate what you’re saying?
Lower humidity should in general equal cooler minimums in the dark hours. You might want to check 2013 minimum temps to get more insight. If minima were still anomalously high in low humidity periods, then that’s going to put a hole in your theory.
bruiser at 19:18 PM on 21 January, 2014
@Barry – Hi Barry, Your map of NSW solar radiation in 2013 would appear to be within the “normal” range however the map itself is something of an anomaly if compared to almost any weather station across Australia. Take the capitals, Alice Springs and fill in the blank spaces at your leisure. They mostly show 6 months of record exposure. 2-4 months above average and 2-4 slightly below average. On balance, they are well above average on an annual basis.
barry at 12:48 PM on 22 January, 2014
Composer, I posted links to BOM solar exposure maps @ 14. The authors of the article state,
…we calculated the probability of hot Australian temperatures in model experiments. These incorporated human (changes in greenhouse gases, aerosols and ozone) and natural (solar radiation changes and volcanic) factors. We compared these probabilities to those calculated for a parallel set of experiments that include only natural factors. In this way, natural and human climate influences can be separated.
If Bruiser has lit upon observations that call that study (and BOM results) into question, then Australia-wide data would be an improvement on unverified claims about two locations. I hope he/she obliges us, because I don’t know where he/she is getting the data from.
Reference please, Bruiser?
bruiser at 08:08 AM on 23 January, 2014
@Barry – Hi Barry, 4th try. The BOM solar radiation data can be accessed at:
http://www.bom.gov.au/climate/data/ Just select solar exposure from the drop-down, enter a town and follow the bouncing ball. When the data comes up you can select “Plot statistics for this year”. Have fun.
bruiser at 08:21 AM on 23 January, 2014
@Skywatcher – The data is very similar for any town. The particularly hot days coincide with low humidity. No surprise there; it was a very dry year. You can plot humidity against max, min, temp difference, cloud cover and solar exposure. My point remains, solar radiation was at record or above average levels for most of 2013. Particularly hot days had low humidity. Whilst a new national average record was set, no new absolute maximum temperature record was set. That record is 50.7 degrees C at Oodnadatta airport 2 Jan 19
barry at 00:24 AM on 25 January, 2014
Bruiser, thanks for the link.
I can confirm that all the capitals and about 20 other stations I checked all had record-breaking solar exposure. I think it’s safe to assume I didn’t just chance upon the outliers. So, I checked the colour maps against the values and they are a good fit. The problem, I realize, is that the resolution of the colour maps is too coarse. The difference between average and record-breakers is 1-2 Mj/sq/m, but the colour maps are in increments of 2 Mj/sq/m (annual), and 3 Mj/sq/m (long-term average). So, I withdraw the earlier comparison. I could find no national average data, so can’t crunch the numbers.
bruiser at 21:03 PM on 25 January, 2014
Hi Barry, my check of values against the map found only one where the actual record was within the correct colour band; some were over 2MJ above the displayed range. 2Mj/sq M/day is an enormous amount of additional energy if you accept the calculation that just 0.4 W/sq M is sufficient to cause the decline in arctic ice. (energy required to melt 290 cubic Km of ice).
Diurnal variation has interested me for a few years. For example, if you plot the data for Melbourne, you get a steady decline. However, the Melbourne weather station is situated at a busy city intersection. I plotted the data for Laverton on the same graph and there is a great correlation until about 1964 when there is a steady roll-off in the Melbourne values. No such decline occurs for Laverton – suggesting that the Melbourne site was affected by some form of development.
barry at 00:06 AM on 26 January, 2014
Bruiser, I rechecked the 2013 solar exposure map against annual values for various locations again and you are quite right. The colour bands do not match the local values. I will email BOM about it.
2Mj/sq M/day is an enormous amount of additional energy if you accept the calculation that just 0.4 W/sq M is sufficient to cause the decline in arctic ice. (energy required to melt 290 cubic Km of ice).
How then would you explain that 4 out of 7 states had warmest years when before 2013, when solar exposure was not highest (in the capitals)?
Following are the warmest years by state, and solar exposure data value (Mj/sq M/day) for that year, ranked from highest, per single locational in the capitals, then record solar exposure (2013) in bold. Period is 1990 – 2013 incl. which is all the solar exposure data we have.
barry at 00:06 AM on 26 January, 2014
Bruiser, I rechecked the 2013 solar exposure map against annual values for various locations again and you are quite right. The colour bands do not match the local values. I will email BOM about it.
SUPPORTING EVIDENCE
3. I have been tracking solar exposure data on the BOM web site throughout 2013. It appears from about August onwards that the BOM was tracking for record levels across the country. The records only go back to 1990 (not a large sample). Table 1. displays the monthly totals for 2013 and the mean for all capital cities, Alice Springs, Derby and Cairns.
| Location/Mean | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | 2013/Mean |
| Sydney 2013 | 21.3 | 19.4 | 17.3 | 15.3 | 12.3 | 8.1 | 12 | 16.6 | 21.5 | 27.3 | 23.9 | 30.5 | 18.8 |
| Mean | 23 | 20.1 | 17.3 | 13.8 | 10.2 | 8.5 | 9.7 | 13.4 | 17.2 | 20.3 | 22.1 | 23.8 | 16.6 |
| Melbourne 2013 | 25.7 | 22.4 | 16.5 | 12.3 | 8.4 | 7.1 | 8.2 | 11.2 | 17 | 20.2 | 23.5 | 27.5 | 16.7 |
| Mean | 24.6 | 21.4 | 16.8 | 11.4 | 7.6 | 6.1 | 6.9 | 9.9 | 13.5 | 18.2 | 22.1 | 24.6 | 15.3 |
| Adelaide 2013 | 27.6 | 23.7 | 17.4 | 15.6 | 10.9 | 8.7 | 9.7 | 13.1 | 18.9 | 26.1 | 30.4 | 31 | 19.4 |
| Mean | 27.7 | 24.4 | 19.5 | 13.7 | 9.4 | 7.5 | 8.4 | 11.6 | 15.6 | 20.9 | 25.3 | 27.1 | 17.6 |
| Hobart 2013 | 22.5 | 20.8 | 14.1 | 10.1 | 7.2 | 4.7 | 5.9 | 9.9 | 15.2 | 21.1 | 21 | 27.7 | 15.0 |
| Mean | 23.6 | 19.9 | 14.8 | 9.4 | 6.2 | 4.6 | 5.4 | 8.4 | 12.9 | 17.6 | 21.5 | 24.3 | 14.1 |
| Perth 2013 | 27.9 | 25.3 | 19.8 | 15.3 | 12.9 | 11.7 | 11.9 | 14.7 | 19.3 | 28.3 | 31.7 | 35.2 | 21.2 |
| Mean | 29.4 | 25.9 | 21.1 | 15.3 | 11.3 | 9.3 | 10 | 13.1 | 17.2 | 23.1 | 27.2 | 30.4 | 19.4 |
| Darwin 2013 | 19.9 | 21.3 | 19.1 | 25.6 | 22.4 | 21.7 | 23.8 | 27.2 | 29.4 | 29.2 | 24.1 | 20.9 | 23.7 |
| Mean | 19 | 19.2 | 21.1 | 22.1 | 20.6 | 19.8 | 20.7 | 22.9 | 24 | 24.3 | 23.2 | 20.9 | 21.5 |
| Brisbane | 21.8 | 17.9 | 16.6 | 18.4 | 14.1 | 11.9 | 12.9 | 20.7 | 23.6 | 28 | 27.8 | 29.9 | 20.3 |
| Mean | 23.7 | 21 | 19.1 | 16.4 | 13.3 | 11.7 | 13 | 16.2 | 19.8 | 22 | 24 | 24.7 | 18.7 |
| Canberra | 26.4 | 19.6 | 19.2 | 15.5 | 11.5 | 8.5 | 9.6 | 14.9 | 20.3 | 27.2 | 27.8 | 31.7 | 19.4 |
| Mean | 25.9 | 22.2 | 18.9 | 14 | 10.2 | 7.9 | 8.9 | 12.4 | 16.8 | 21.3 | 24.4 | 26.7 | 17.5 |
| Alice Springs | 28.1 | 23.6 | 22.4 | 23 | 17.4 | 14.6 | 18.4 | 23.4 | 27.3 | 31.1 | 29.4 | 28 | 23.9 |
| Mean | 27.1 | 25.1 | 23.7 | 20.4 | 16.4 | 14.7 | 15.9 | 19.1 | 22.5 | 25.4 | 27 | 27.2 | 22.0 |
| Cairns | 20.6 | 21.4 | 17.7 | 20 | 16 | 17.1 | 17 | 23.7 | 27.3 | 27.6 | 23.6 | 27.3 | 21.6 |
| Mean | 21.7 | 20 | 19.4 | 18.2 | 16.2 | 15.3 | 16.1 | 19 | 22.5 | 24 | 23.9 | 23.4 | 20.0 |
| Derby | 24.1 | 20.9 | 23.1 | 22.7 | 19.9 | 17.4 | 20.5 | 24.5 | 29.9 | 31 | 31.3 | 26.2 | 24.3 |
| Mean | 23.3 | 22.6 | 22.9 | 21.8 | 18.9 | 17.3 | 18.6 | 21.6 | 24.7 | 26.8 | 27.4 | 25.3 | 22.6 |
Table 1. Solar exposure MJ/M2/Day
4. The BOM chart graphs for Sydney provide evidence of the BOM revisions. The first graph was downloaded in Jan 14 whilst the second is the same graph downloaded in May 14:
Product Code: IDCJAC0016
Jan 2014
Product Code: IDCJAC0016
Page created: Thu 08 May 2014 14:01:04 PM EST
5. The difference in the two charts provides insight into the revisions. The first chart clearly shows record levels of solar exposure for May, July, August, September, October and December. The second chart does not have any record months. The extent and degree of the data revisions in the Sydney chart, the data for all the cities is displayed in Table 1. Table 2 has been conditionally formatted to show where the BOM has increased the levels of solar exposure (coded pink) and decreased solar exposure (coded blue). The conditional formatting clearly highlights that solar exposure data was increased slightly for January, February and March and decreased dramatically for the rest of the year.
Table 2:
| Location/Mean | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | 2013/Mean |
| Sydney 2013 | 21.3 | 19.4 | 17.3 | 15.3 | 12.3 | 8.1 | 12 | 16.6 | 21.5 | 27.3 | 23.9 | 30.5 | 18.8 |
| Mean | 23 | 20.1 | 17.3 | 13.8 | 10.2 | 8.5 | 9.7 | 13.4 | 17.2 | 20.3 | 22.1 | 23.8 | 16.6 |
| 1-May-14 | 21.8 | 20.1 | 17.9 | 13.7 | 10.8 | 6.9 | 10.3 | 14.1 | 17.1 | 22 | 18.7 | 25.3 | 16.6 |
| Variation | 0.5 | 0.7 | 0.6 | -1.6 | -1.5 | -1.2 | -1.7 | -2.5 | -4.4 | -5.3 | -5.2 | -5.2 | -2.2 |
| Variation % | 2 | 4 | 3 | -10 | -12 | -15 | -14 | -15 | -20 | -19 | -22 | -17 | |
| Melbourne 2013 | 25.7 | 22.4 | 16.5 | 12.3 | 8.4 | 7.1 | 8.2 | 11.2 | 17 | 20.2 | 23.5 | 27.5 | 16.7 |
| Mean | 24.6 | 21.4 | 16.8 | 11.4 | 7.6 | 6.1 | 6.9 | 9.9 | 13.5 | 18.2 | 22.1 | 24.6 | 15.3 |
| 1-May-14 | 26.9 | 23.6 | 17 | 11 | 7.5 | 6.1 | 7 | 9.1 | 13.1 | 15.3 | 18.3 | 22.5 | 14.8 |
| Variation | 1.2 | 1.2 | 0.5 | -1.3 | -0.9 | -1 | -1.2 | -2.1 | -3.9 | -4.9 | -5.2 | -5 | -1.9 |
| Variation % | 5 | 5 | 3 | -11 | -11 | -14 | -15 | -19 | -23 | -24 | -22 | -18 | |
| Adelaide 2013 | 27.6 | 23.7 | 17.4 | 15.6 | 10.9 | 8.7 | 9.7 | 13.1 | 18.9 | 26.1 | 30.4 | 31 | 19.4 |
| Mean | 27.7 | 24.4 | 19.5 | 13.7 | 9.4 | 7.5 | 8.4 | 11.6 | 15.6 | 20.9 | 25.3 | 27.1 | 17.6 |
| 1-May-14 | 29.1 | 25.1 | 18 | 14 | 9.7 | 7.4 | 8.3 | 10.9 | 14.8 | 20.9 | 25 | 25.9 | 17.4 |
| Variation | 1.5 | 1.4 | 0.6 | -1.6 | -1.2 | -1.3 | -1.4 | -2.2 | -4.1 | -5.2 | -5.4 | -5.1 | -2.0 |
| Variation % | 5 | 6 | 3 | -10 | -11 | -15 | -14 | -17 | -22 | -20 | -18 | -16 | |
| Hobart 2013 | 22.5 | 20.8 | 14.1 | 10.1 | 7.2 | 4.7 | 5.9 | 9.9 | 15.2 | 21.1 | 21 | 27.7 | 15.0 |
| Mean | 23.6 | 19.9 | 14.8 | 9.4 | 6.2 | 4.6 | 5.4 | 8.4 | 12.9 | 17.6 | 21.5 | 24.3 | 14.1 |
| 1-May-14 | 23.1 | 21.8 | 14.2 | 8.9 | 6.3 | 4 | 5 | 8.1 | 11.4 | 16.1 | 15.9 | 22.4 | 13.1 |
| Variation | 0.6 | 1 | 0.1 | -1.2 | -0.9 | -0.7 | -0.9 | -1.8 | -3.8 | -5 | -5.1 | -5.3 | -1.9 |
| Variation % | 3 | 5 | 1 | -12 | -13 | -15 | -15 | -18 | -25 | -24 | -24 | -19 | |
| Perth 2013 | 27.9 | 25.3 | 19.8 | 15.3 | 12.9 | 11.7 | 11.9 | 14.7 | 19.3 | 28.3 | 31.7 | 35.2 | 21.2 |
| Mean | 29.4 | 25.9 | 21.1 | 15.3 | 11.3 | 9.3 | 10 | 13.1 | 17.2 | 23.1 | 27.2 | 30.4 | 19.4 |
| 1-May-14 | 29.4 | 26.9 | 20.8 | 13.7 | 11.5 | 10.2 | 10.1 | 12.4 | 15 | 22.9 | 26.2 | 30 | 19.1 |
| Variation | 1.5 | 1.6 | 1 | -1.6 | -1.4 | -1.5 | -1.8 | -2.3 | -4.3 | -5.4 | -5.5 | -5.2 | -2.1 |
| Variation % | 5 | 6 | 5 | -10 | -11 | -13 | -15 | -16 | -22 | -19 | -17 | -15 | |
| Darwin 2013 | 19.9 | 21.3 | 19.1 | 25.6 | 22.4 | 21.7 | 23.8 | 27.2 | 29.4 | 29.2 | 24.1 | 20.9 | 23.7 |
| Mean | 19 | 19.2 | 21.1 | 22.1 | 20.6 | 19.8 | 20.7 | 22.9 | 24 | 24.3 | 23.2 | 20.9 | 21.5 |
| 1-May-14 | 19.9 | 22.2 | 19.9 | 23.1 | 20 | 19.1 | 20.6 | 23.6 | 24.4 | 23.8 | 18.8 | 15.8 | 20.9 |
| Variation | 0 | 0.9 | 0.8 | -2.5 | -2.4 | -2.6 | -3.2 | -3.6 | -5 | -5.4 | -5.3 | -5.1 | -2.8 |
| Variation % | 0 | 4 | 4 | -10 | -11 | -12 | -13 | -13 | -17 | -18 | -22 | -24 | |
| Brisbane | 21.8 | 17.9 | 16.6 | 18.4 | 14.1 | 11.9 | 12.9 | 20.7 | 23.6 | 28 | 27.8 | 29.9 | 20.3 |
| Mean | 23.7 | 21 | 19.1 | 16.4 | 13.3 | 11.7 | 13 | 16.2 | 19.8 | 22 | 24 | 24.7 | 18.7 |
| 1-May-14 | 22.4 | 18.4 | 17 | 16.6 | 12.5 | 10.3 | 11.1 | 17.8 | 19.1 | 22.7 | 22.4 | 24.7 | 17.9 |
| Variation | 0.6 | 0.5 | 0.4 | -1.8 | -1.6 | -1.6 | -1.8 | -2.9 | -4.5 | -5.3 | -5.4 | -5.2 | -2.4 |
| Variation % | 3 | 3 | 2 | -10 | -11 | -13 | -14 | -14 | -19 | -19 | -19 | -17 | |
| Canberra | 26.4 | 19.6 | 19.2 | 15.5 | 11.5 | 8.5 | 9.6 | 14.9 | 20.3 | 27.2 | 27.8 | 31.7 | 19.4 |
| Mean | 25.9 | 22.2 | 18.9 | 14 | 10.2 | 7.9 | 8.9 | 12.4 | 16.8 | 21.3 | 24.4 | 26.7 | 17.5 |
| 1-May-14 | 27.5 | 20.4 | 20.1 | 14 | 10.2 | 7.3 | 8.2 | 12 | 15.9 | 21.7 | 22.2 | 26.4 | 17.2 |
| Variation | 1.1 | 0.8 | 0.9 | -1.5 | -1.3 | -1.2 | -1.4 | -2.9 | -4.4 | -5.5 | -5.6 | -5.3 | -2.2 |
| Variation % | 4 | 4 | 5 | -10 | -11 | -14 | -15 | -19 | -22 | -20 | -20 | -17 | |
| Alice Springs | 28.1 | 23.6 | 22.4 | 23 | 17.4 | 14.6 | 18.4 | 23.4 | 27.3 | 31.1 | 29.4 | 28 | 23.9 |
| Mean | 27.1 | 25.1 | 23.7 | 20.4 | 16.4 | 14.7 | 15.9 | 19.1 | 22.5 | 25.4 | 27 | 27.2 | 22.0 |
| 1-May-14 | 29.8 | 25.1 | 23.5 | 20.7 | 15.2 | 12.3 | 15.8 | 20 | 22.3 | 25.3 | 23.7 | 22.6 | 21.4 |
| Variation | 1.7 | 1.5 | 1.1 | -2.3 | -2.2 | -2.3 | -2.6 | -3.4 | -5 | -5.8 | -5.7 | -5.4 | -2.5 |
| Variation % | 6 | 6 | 5 | -10 | -13 | -16 | -14 | -15 | -18 | -19 | -19 | -19 | |
| Cairns | 20.6 | 21.4 | 17.7 | 20 | 16 | 17.1 | 17 | 23.7 | 27.3 | 27.6 | 23.6 | 27.3 | 21.6 |
| Mean | 21.7 | 20 | 19.4 | 18.2 | 16.2 | 15.3 | 16.1 | 19 | 22.5 | 24 | 23.9 | 23.4 | 20.0 |
| 1-May-14 | 20.7 | 22.4 | 18.3 | 18 | 14.2 | 15 | 14.6 | 20.5 | 22.5 | 22.3 | 18.1 | 22.1 | 19.1 |
| Variation | 0.1 | 1 | 0.6 | -2 | -1.8 | -2.1 | -2.4 | -3.2 | -4.8 | -5.3 | -5.5 | -5.2 | -2.6 |
| Variation % | 0 | 5 | 3 | -10 | -11 | -12 | -14 | -14 | -18 | -19 | -23 | -19 | |
| Derby | 24.1 | 20.9 | 23.1 | 22.7 | 19.9 | 17.4 | 20.5 | 24.5 | 29.9 | 31 | 31.3 | 26.2 | 24.3 |
| Mean | 23.3 | 22.6 | 22.9 | 21.8 | 18.9 | 17.3 | 18.6 | 21.6 | 24.7 | 26.8 | 27.4 | 25.3 | 22.6 |
| 1-May-14 | 25 | 21.8 | 24.5 | 20.5 | 17.7 | 15.1 | 17.7 | 21.1 | 24.8 | 25.5 | 25.8 | 21 | 21.7 |
| Variation | 0.9 | 0.9 | 1.4 | -2.2 | -2.2 | -2.3 | -2.8 | -3.4 | -5.1 | -5.5 | -5.5 | -5.2 | -2.6 |
| Variation % | 4 | 4 | 6 | -10 | -11 | -13 | -14 | -14 | -17 | -18 | -18 | -20 |
6. In addition, a comparison of the Sydney complete data set indicates that the revision of data by the BOM extends back to 1 July 2011. Clearly, the BOM altered the data some time between Jan 14 when Barry undertook to e-mail them about the discrepancy and May 2014 when I revisited the issue.
7. For your considered response.
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Caught in the act.
Honest Data to a warmist is like Kryptonite to Superman.
So what explanation has been given for the change?
sons of bitches!
So being an annoying, intellectually empty pest gets the “G” rewarded with its name in a top-level post. Now you’ve done it.
The b*st*rds always win… until they don’t.
Keep pushing, keep exposing, keep up the good work, Bruiser.
EC, he got kicked around pretty good on the previous post. I did some of the kicking. Good for amusement.
Incredible. But, proven by your fine research.
Thank you, “Bruiser.”
Way — to — go, “Bruiser”!
BASH that corrupt BOM!
http://1.bp.blogspot.com/-wjKRpPMnvh4/T82nNfde48I/AAAAAAAAAJU/eqKp0V2o7Bo/s1600/BruiserBomBashGameSlide.jpg
Great work BM.
Keep it up!
Interesting work. Thanks for sharing. Would be interested to know how/if this line of inquiry continues.
Obviously it must be due to a convenient TOBS adjustment…/sarc
Standard process in climate sciences seems to be to adjust inconvenient data when it controverts the narrative.
(Sigh.) TOBS is real. Just is.
But I’d be interested to know how this was done. Pairwise homogenization, perhaps? (What could possibly go wrong?)
Sure, but so is UHI.
It is hard to think of any kind of instrument calibration revision which would increase radiation in some months and reduce it in the others so this does look very suspicious. Can anyone provide any information on the instrumentation used for these measurements?
These are simple standard radiometers. There are several manufacturers. Kipp and Zonen in Germany is the one illustrated in the climate chapter of The Arts of Truth.
Computing global solar radiation is not simple. BoM gives an account here (more here). They have to measure separately and combine direct and diffuse. Calibration isn’t simple either. The document describes a recent change from the “Component Sum method” to the “Alternate method”.
The comparison here is between estimates for 2013 data produced prior to Jan 14, 2014, and those some time later. I think a revision of that early estimate is not surprising.
That explanation sounds a lot like ‘Mikes’s nature trick’
The question then becomes: were the years that this was compared against reprocessed in the same manner? If not, then the whole kit and kiboodle needs to be tossed in the nearest bin. Apples compare to oranges only when speaking of fruit.
When techniques continuously change in midstream, there are going to be questions. Of course they really should have left these as the direct measure and just compared what was read out from the instrument. That way you are always comparing apples to apples and not apples to xylophones. The diffuse environment only changes if there is a change to the surroundings, otherwise the same surfaces will scatter energy in the same manner every day. It is almost irrelevant for trend analysis, and as climate analysis almost exclusively deals with the divergence from the baseline, we are only interested in the trends.
NS, whether true or not that is not this post. Thismis insolation measured at specific weather stations in Australia. Nothing to do with global. Nice straw man diversion.
Nonsense. Calculating daily solar insolation values for ANY latitude (not just Australia) and ANY day-of-year is simple, provided accurate calibration data for the specific region in question (the measured atmospheric attenuation values) are available for the season (day) in question for the specific region analyzed. Same latitude, different dust, pollen, cloud cover, direct/diffuse ratio, pressure, humidity, wind direction? Need a different calibration data series.
But that source page itself claims bad accuracy and no calibration since 2003. Further,
”
Satellite problems occasionally mean that the solar radiation data are not available. During an outage the grid files are generated and provided but missing data are flagged with the special value of -99.99.
Data are unavailable for 12/11/2009 and from 16/11/2009 onwards, owing to problems with the MTSAT-1R satellite.”
“…Computing global solar radiation is not simple. BoM gives an account here (more here)…”
I’ll say. The second ‘here’ states that satellite data ‘are unavailable for 12/11/2009 and from 16/11/2009 onwards, owing to problems with the MTSAT-1R satellite…’
“…I think a revision of that early estimate is not surprising…”
Are you missing the point entirely or knowingly diverting away from it?
ristvan
“Nothing to do with global.”
Read the heading on the graph he provided. “Sydney global solar exposure”. It means all-round.
Owen in GA
“The question then becomes: were the years that this was compared against reprocessed in the same manner?”
The graph shows that not only did the 2013 value change, but the long term normal. So I think the answer is yes.
“Are you missing the point entirely”
I may be. All I see is a whole lot of people getting excited about the BoM revising some figures that they have never looked at before, and seem rather unsure about what they mean. And concluding that this is all some sort of cheat to boost the global warming narrative (How?). What am I missing?.
I have to agree with Nick on this one.
I wasn’t surprised in the least to hear the Climate Faithful had adjusted away more inconvenient data. ~_~
There is no indication at the links given which claims that past values have been reworked. Alternate Calibration has nothing whatever to do with changing past values, only on the current calibration of instruments.
Kip,
“on the current calibration of instruments”
No, if you recalibrate the instruments, and they are the same ones that were used in the past, then the past readings should be corrected to the new calibration.
Schitzree,
“Climate Faithful had adjusted away more inconvenient data”
I have yet to see anyone explain why they would find it inconvenient. Or indeed why the CF, or anyone else, would have noticed it.
“I have yet to see anyone explain why they would find it inconvenient.”
Because many billions of dollars in research grants to “prove” a trace gas that is entirely beneficient is actually going to destroy ‘Civilisation As We Know It’ or possibly even the entire planet are dependent on it, and the debunking of it would cause a lot of charlatans, parasites and trough-snouters who would be otherwise unemployable to have to try to get real jobs?
Nick Stokes, “No, if you recalibrate the instruments, and they are the same ones that were used in the past, then the past readings should be corrected to the new calibration.”
No, the past readings should not be “corrected.” The instruments were calibrated in the past, which calibrations were applied to those past readings.
Calibrations change with time, and the new corrections should be applied only to those measurements taken subsequent the calibration. One never, ever, applies present calibrations to past measurements already corrected using past calibrations. Never. Ever.
One lives with whatever measurement disparities emerge, and with the uncertainty associated with them. Except in Nickstrokesland, where all instruments have infinite resolution.
NS, “No, if you recalibrate the instruments, and they are the same ones that were used in the past, then the past readings should be corrected to the new calibration.”
So how do you tell when the instrument dropped out of calibration? You need to know how far back to apply “corrections”. Was it an abrupt change or a more subtle one over time?
G’ Day Nick, suggested that you look at the Table 2 adjustments. If you can provide a rationale explanation of how January, February and March are increased while the rest of the year is decreed by a much greater factor.; then I will go back to thinking that the BOM is a respectable scientific organization.
Bruiser,
“If you can provide a rationale explanation of how January, February and March are increased while the rest of the year is decreed by a much greater factor”
It seems to be a systematic seasonal variation throughout. I have no basis for second-guessing the reason, and neiher do you. But I see no reason for surprise at seasonal variation.
But it seems that the answer may be at that link I cited. They say:
“For the initial calibration at the Bureau of Meteorology the ‘Component Sum Method’ is used. The ‘Alternate Method’ has recently been adopted as the primary calibration method for the Baseline Surface Radiation Network, and Dr. Forgan recently received the 12th WMO Vaisala Award for his paper outlining the method. Both methods rely on measurements of global and diffuse irradiance by pyranometers and direct irradiance by pyrheliometers.”
So it seems that they do use the different methods in sequence. And they say:
“Each year the Bureau’s Radiation Unit staff visit field sites to calibrate the on-site pyrheliometer and to swap (alternate) the global and diffuse pyranometers. In this way, between visits the pyranometer being used to measure global irradiance is being calibrated every clear sky day, prior to it being swapped for use as a diffuse pyranometer.”
It sounds like they need to collect that data in order to apply their final calibration, using the alternate method. It looks to me as if that annual tour led to the difference, and the May figures are from the BSR Network.
A lot of people here arguing because calibration has changed, this is data ‘manipulation’. I wonder if they feel the same about Roy Spencer’s UAH V6.0. That data set has needed to be calibrated at least 6 times. Re-calibration, even the dreaded homogenization, can make data more accurate than previous data, so shouldn’t be dismissed out of hand.
Mat, UAH has detailed reasons for re-calibration. Compare that to Nick’s “It sounds like they need to collect that data in order to apply their final calibration”.
Does that sound like they have provided detailed reasons?
As far as I can see, when all is said and done, the error bars on these solar radiation figures is so large as to make them almost useless for the types of detailed analysis they are being used for. Too much “educated guessing” and post measurement calibration going on for me to think otherwise.
BTW I’d like to know how legit the sudden increase in temperature in mid-afternoon for Brisbane on November 16 2014 was. By ‘coincidence’ the G20 meeting (accompanied by an Obama speech on climate) was held in Brisbane that day and a record high temperature was predicted. Got nowhere near the predicted temperature until a magical hot wind suddenly arrived in mid-afternoon (such winds are not unprecedented in Brisbane but was it more than coincidence).
You explained it in the first bit: Obama gave a speech – that is always good for a burst of hot air.
Blatantly unethical (under Australian law, criminal?).
So say all bona fide scientists.
A typical example (from transcript of video lecture here: https://prezi.com/u1cgkqxrzmar/ethics-in-forensic-science/ ):
Thank you, Janice.
Suggestion: next time also boldface the line Maintain the integrity of the evidence. That’s critically important, as Phil Jones tacitly admitted when he indicated he might destroy data rather than provide it: “I think I’ll delete the file rather than send to anyone.”
Cheers
Phil
(well, I had to choose one or the other (to suit my style preference) — GOOD POINT, though, Ralph).
It must have been a power grid failure.
Nicely done. The only criticism is the acknowledgment of “Griff”. To contaminate such a high calibre piece of work with a reference to such a low IQ is a shame. Perhaps a footnote reference in 4pt font would have been more appropriate.
Agreed, Mr. Ranger.
4pt and accompanied by a picture of the little troll:
http://static.giantbomb.com/uploads/original/0/1992/1866603-griff_card_1.gif
Age: 14. What a surprise.
I must say, though, that last line softened my perspective. Top billing — NO, but, well, shoot. Griff — you’re welcome to hang out here (if I may be so bold as to speak on behalf of the WUWTers), “where everybody knows your name.”
I thought Griff has claimed to be British.
lol, yes, Mark, I believe he has. He also said he has a wife. 🙂
He may have claimed to be British, but with his level of accuracy, it’s a stretch to assume there’s much change he’s correct.
Of British descent maybe? that’s pretty broad. or maybe he meant He’s English SPEAKING.
Griff thinks he speaks American…..
I really don’t like it when regulars here insult Griff like this. First, he probably likes it. Second, he does a fine job illustrating his intelligence level all by himself. And third, it only diminishes the insult thrower. My advice: don’t wrestle with pigs.
Looks like you caught them out. Essay When Data Isn’t in ebook Blowing Smoke caught NOAA out in their CONUS state by state reporting in the switch from Drd964x to nClimDiv at the beginning of 2014. See my guest post here in the David Rose series. Site search Istvan NClimDiv takes you there straightway.
It is important to fight to preserve the record. Thank you for documenting this institutional vandalism so well.
This has also happened with other areas of Australian meteorological history.
Jennifer Marohasy has documented uncalled for adjustments of the temperature records in Australia multiple times.
http://jennifermarohasy.com/
Yes. Rutherglen was shocking. And then BOM lied about station moves as an excuse. And then retired Rutherglen researcher Dr. Bill johnson confirmed that there had been no such events. Another example in essay When Data Isn’t.
I think I can explain ACORN-SAT errors as methodological flaws, but still a work in progress:
https://climanrecon.wordpress.com/2017/03/02/climate-distortion-in-acorn-sat/
My own research suggests you are on the right track. Any version of ‘regional expectations’ (BEST) aka pairwise homogenizarion (NOAA) contaminates godd stations with bad ones. The reason is very simple: as the US Surfacestations.org project showed conclusively, there are many more bad stations than good ones. Hope this logic helps. See also my guest post here on the US CRN 1 subset from surfacestaions.org. How good is Nasa GISS? In 2015. You are welcome to include it in your own work.
Rud, I believe that there are two distinct problems with ACORN-SAT (probably applies to other homogenisations as well):
1. Stations with contrary trends being “bullied” by the neighbours into changes whenever a change in the weather occurs which triggers the interest of the step change detectors. This is particularly prevalent in Australia, where interior stations have very few interior neighbours, mostly ones near the coast, the latter imposing their trends on the interior ones via use of medians, which hands victory to the majority.
2. THIS IS A BIGGIE: All step changes in temperature are regarded as being persistent errors, which is clearly wrong for mid-century urban heating, and any screen degradation followed by replacement. Not a big deal back in the day when they only went back to 1950, but hopelessly inadequate when going back to 1910.
I have realized the same possibility, but I have no evidence for more positive saw teeth compared to negative. Otoh, I have no good reasons to believe the two types of sawteeth should cancel out. Good work, please read the literature before declaring victory.
Solar exposure over the midwest US has been lackluster from observation where I live for a year or so now. I’m still waiting for a completely clear day in 2017.
There’ll need to be some adjustments.
Data fiddlers aka quacks.
Feynman said if the data doesn’t fit the the theory, the theory is wrong and must be changed.
In climate science if the data doesn’t fit the theory, the data is wrong and must be changed.
And then we have Mann and paleoclimate, where both the data and the theory are wrong.
Hmm… 3 analytical systems, one of which is traditional scientific method and the other two are products of the modern progressive policy movement (flashback to Sesame Street; “One of these things is not like the others” song). Two are politically correct by past practice and one has been largely ignored.
Can anyone guess which one holds the key to enlightenment of future generations?
Another case of Photoshopping data to present the picture the data keepers want to support. Nice job Bruiser.
I have seen into the minds of our world’s climate data keepers and it says this – “There is no such thing as data that doesn’t support your theory, there is only data that hasn’t been adjusted enough to support your theory”
Even if you catch them red handed they won’t admit they are wrong.
What caused the conditions that permitted more solar radiation to reach ground level? It must have been CO2. It’s obviously just another example of climate change.
Some of you guys are insufficiently cynical. Bah Humbug!
Which are you going to believe? The cunning contrivance you see? Or
the data?
(also for Richard, et al. to bring a little bon homme-restoring levity to some hot-under-the-collar scientists)
Yes. This is really funny.
(youtube — lip sync “men’s” gospel quartet 🙂 “Christmas Carol of Love” or something like that)
#(:))
That stole the show- Gar-on-tee!
🙂
Surely this is worthy of a grilling in a Senate Estimates hearing?
I am appalled at the shocking revelations presented above. Rather than just Senate Estimates Committee, as an Australian I think we urgently need a Royal Commission into this matter
Rather than investigations into trivial matters such as institutionalized chìld abuse or into financial malfeasance by the banks, we must divert these resources into who was responsible for this scandal. The discovery of the BOM’s fabrication of this data, which is the lynchpin for global warming fraud, will destroy the hoax in its entirety.
Surely Griff will have the good grace to say thank you for being shown the truth and apologise for being doubtful. While he’s at it he should apologize for his disgraceful belittling of Lombok, Anthony, and others earlier today on another WUWT thread. A very poor show I thought.
He reserves particular venom for Professor Judith Curry and Doctor Susan Crockford in particular, because his attempt to traduce her professsional credibility backfired on him in particularly spectacular fashion courtesy of Climate Otter. Seems along with being a paid propagandist for Big Wind posting from a corporate IP he’s a misognyst to boot.
Griffs one of the endless AGW barking hicks who never can tell me the name of the law of thermodynamics to determine the temperature of some air;
he can’t tell me the equation;
he can’t tell me what any of the factors mean;
he can’t explain which part he thinks has the ”magic gais powur”.
He’s like every AGW fraud who ever said it could have been real: a FAKE.
That’s why people spit on AGW frauds on sight to their face. We hate fake science.
The word is hate.
REAL scientists HATE fake science.
[You have 5 different user-id’s. Chose one. .mod]
I was about to say, he’s not gonna risk a paycheck just to make us happy and show “good grace”.
Global Warmistas torture the data until it confesses just exactly what they want to hear. Temperature, Solar exposure, rainfall, ice extent, ice thickness, hurricanes, sea level, glacier movement, coral welfare, sinking island, endangered species and crop results, all are subject to the rule of the Warmista adjusters.
Blastedely complex models so it is nigh impossible to reproduce output? If only Pons and Fleishmann would have been so wise (sarc off).
That should be Lomborg of course, wretched spell checker keeps changing spellings. My apologies.
Sloppy, Sloppy, Sloppy.
To anybody who uses the hopelessly lame excuse of “my spellchecker made me do it”, I have one thing to say:
LUCK YOU!, … er… DUCK YOU! … er… or something.
Remember, Spell Check is your friend.
…the kind of friend who ‘borrows’ your car while drunk and leaves it in a ditch.
Senate committees are stacked in favour of greens and labor on such issues , last time BOM was caught with its hands in the cookie jar there was a 0.5 millisecond uproar .
BOM just threw statistics and their version of excuses which the entire political cabals accepted .