OpenNEM Crashes- Atlassian Software Fault?

Remember on Monday the OpenNEM showed Rooftop Solar generation dropping out?  It has happened again just three days later:

Not only that, but the whole OpenNEM reporting system seems to have crashed.  The last update was at 1:40 pm on Thursday 26^th.  This screenshot was at 9.00 am this morning Friday.

It looks like a software system crash.  Not in electricity production or we’d have noticed, but in the reporting.

This website (OpenNEM) is not that of the actual NEM, but has been set up to make NEM data “more accessible to a wider audience”.  That’s very commendable.  Note who has set it up:

Simon Holmes a Court was instrumental in the Teal wins over moderate Liberals.  He’s pushing rapid transition to renewables.

Dr Dylan McConnell is an energy systems researcher at the Climate and Energy College at the University of Melbourne.

Nik Cubrilovic is an internet security blogger, best known for computer hacking, according to Wilipedia.

And the platform is driven by Atlassian, founded and largely owned by Michael Cannon-Brookes, who is the largest shareholder of AGL and is trying to stop the proposed AGL demerger so that he can get rid of fossil fuels faster.

Couldn’t happen to nicer blokes….

Or maybe they have interests in uranium mines and know that nuclear is the only hope for Net Zero?

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What Happened To Rooftop Solar Yesterday?

Yesterday, 23 May, something strange happened to electricity supplies across the National Energy Market (NEM).

Figure 1 shows total electricity generation for the last three days across Queensland, New South Wales, Victoria, South Australia, and Tasmania.  Notice the huge drop in generation early yesterday afternoon.

Figure 1:  3 Day Generation, NEM

The drop was entirely due to Solar Rooftop generation going from gangbusters at 1:00 pm to zero from 2:00 pm to 3:00 pm.

Figure 2:  All NEM Generation Monday 23^rd.

Figures 3, 4, and 5 show the drop in closer detail.

Figure 3:  All NEM Generation 1:00 pm

Figure 4:  All NEM Generation 2:00 pm

Figure 5:  All NEM Generation 3:30 pm

It happened in every state, from Queensland, producing the most solar power (1,376 MW or 18.6% of the Queensland total):

Figure 6:  Queensland Rooftop Solar:

to South Australia, whose paltry 814 MW was 48.4% of total power used.  Interesting that solar in SA fell off from 12:30 pm.

Figure 7:  South Australia Rooftop Solar (12:30 pm):

At 2:00 pm, the drop in energy supply was nearly half (1,659 MW to 849 MW)- and they were still charging batteries.

Figure 8:  South Australia Rooftop Solar (2:00 pm):

By 3:30 pm, SA solar had recovered to 28% of supply- which was also helped by an almost equal amount of imported electricity:

Figure 9:  South Australia Rooftop Solar (3:30 pm):

In case you think this was caused by the cloudy weather over eastern Australia, it wasn’t:  it was mostly clear.

Figure 10:   BOM radar map at 1.30 pm 23^rd May

Network generation fell by 16.5% from 1:00 pm to 2:00 pm.  Did no one notice?  Were there no blackouts?  Why was all rooftop solar in eastern Australia closed down for an hour?  Did you know they could do that?  If rooftop solar can be completely shut down without any ill effects why have it in the first place?

I think this will remain a mystery.

Australian Temperature- Satellites or Surface Stations?

For years we have been very sceptical about the official Bureau of Meteorology (BOM) temperature record which is based on 104 surface stations in the ACORN-SAT (Acorn) network.  In this post I look at one of the main reasons for doubting the veracity of the surface record- the increasing divergence from the satellite record.

First up I should say that the two records should not necessarily agree, because they measure two completely different things.  Surface stations measure the temperature of the air 1.2 metres above the ground and report the highest and lowest one second samples each day at 104 locations.  These are combined in a grid average to give monthly, seasonal, and annual temperatures.  Satellites measure temperatures of the atmosphere from the ground to many kilometres up, every second, over a wide area for each pass.  These are similarly combined by algorithms to calculate a monthly average for (in this case) the land area of Australia’s Temperature of the Lower Troposphere (TLT). 

They are both useful for different purposes but are not easily compared.  Because minimum surface temperatures poorly match TLT, mean surface temperature is also a poor match.  Maxima are a better match, but still not perfect.

For this post I use data from the University of Alabama (Huntsville) (UAH) which calculates anomalies from 1991 to 2020 means.  I have converted Acorn data from anomalies from 1961-1990 means, to anomalies from 1991-2020 means, to match.

Figure 1 shows monthly Acorn maxima data and UAH means from December 1978.

Figure 1: Monthly Surface Tmax and UAH data

Although surface maxima have a much larger range than TLT anomalies, they plainly follow similar trajectories.  12 month running means smooth the data and allow easier visual comparison.

Figure 2: Running 12 Month Means: Surface Tmax and UAH data

Similar, but different at several times.   Annual means show that in some years Tmax and TLT are close to identical, while in other years they have large differences.

Figure 3: Annual Means: Surface Tmax and UAH data

In 2015 I showed the reason for these differences (but not the difference in trends).  The differences between the two datasets are very largely due to variations in rainfall.  In wet years surface maxima are relatively much cooler than TLT; in dry years surface maxima are much warmer.  In Figure 4 I have calculated rainfall anomalies scaled down by a factor of 20 and inverted, to compare with the difference between Tmax and TLT.

Figure 4: Running 12 month Means: Surface Tmax minus UAH and Inverted Rainfall

The match is close.  Figure 5 shows annual values, and trend lines.

Figure 5: Annual Means: Surface Tmax minus UAH and Inverted Rainfall

While annual rain has been slightly increasing (it’s inverted, remember) the relative difference between surface temperature and atmospheric temperature has been increasing at a rate of one degree per hundred years.  That’s odd.  Figure 6 shows the relationship between the temperature difference and rainfall.

Figure 6: Annual Surface Tmax minus UAH versus Scaled Rainfall

For every extra 20mm of rainfall, the difference between surface maxima and TLT decreases by 0.85 degrees Celsius.  The trend lines in Figure 5 should be close to parallel, not diverging.

As well, as rainfall increases, Tmax should decrease, as Figure 7 shows.

Figure 7: Surface Tmax as a Product of Rain

But as we saw in Figure 3, Tmax is increasing faster than UAH.

Furthermore, as surface Tmax increases, TLT should be increasing as well, which it is, but at a slower rate.

Figure 8:  Atmospheric Temperature as a Product of Surface Tmax

Is the atmospheric temperature lagging behind surface temperature?  Figure 9 shows the last two years of monthly values.

Figure 9:  Monthly Atmospheric Temperature and Surface Tmax, January 2020-March 2022

The values are mostly synchronous, with sometimes a delay in one or the other of one month.  (Remember, we are comparing data from 104 stations scattered across the continent, with that of the atmosphere with constantly changing and circulating winds).  When the land warms, the atmosphere warms with it; when the land cools, so does the atmosphere.

Conclusion:

Tmax should not be increasing faster than atmospheric temperature.  There is no real delay in any temperature change, as the atmosphere is heated each day by the land.  Therefore it appears that there must be some fault with the maximum temperatures reported by ACORN-SAT, which appears to be warming too rapidly.

Explanation of the mechanism for rainfall moderation of surface-atmospheric temperature differences:

In wet years more moisture carried upwards condenses, releasing heat, thus warming the atmosphere, while the surface is cooled by cloud cover, evaporation, and transpiration.  In dry years much less moisture is convected, so less heat is released in the atmosphere, while the surface is hotter because of less cloud cover and less evaporation and transpiration.  Thus dry years have a greater relative difference between Tmax and TLT than wet years.

The only energy source is solar radiation heating the land surface in daylight hours, which in turn heats the atmosphere by conduction and convection.  At night as radiation to space rapidly cools the earth, convection also rapidly decreases, so maxima, not minima, are responsible for the relationship with TLT. 

A complication is that in summer (and more so in very wet La Nina years) large volumes of very moist air from the tropical seas to the north converge over northern Australia and penetrate even into southern Australia.  This warm moist air cannot heat the surface but through condensation transfers heat to the upper atmosphere- therefore the difference between surface and atmosphere is even smaller.

Trade Winds and Australian Sea levels

In my post Is Climate Change Threatening the Solomon Islands? I showed that sea levels at Honiara are predominantly caused by variations and strengthening of the south-east trade winds blowing across the Pacific.

Trade wind strength is also an indicator of sea levels all around Australia- as far south as Tasmania.

I use scaled trade wind index data from NOAA, and mean sea level data from the BOM’s Australian Baseline Sea Level Monitoring Project.  Sea level is in metres and all data are monthly anomalies.

Here’s a map showing the location of the ABSLMP stations.

Figure 1:  Sea level stations

I did not use those stations with large gaps (e.g. Thevenard) or very short records (Thursday Island).

Figure 2 shows sea level and the trade wind index (scaled down by a factor of 60).

Figure 2:  Trade Winds and East Coast Sea Levels

Sea levels appear to loosely match trade winds (a symptom of the El Nino- Southern Oscillation-ENSO).  Sea levels are averaged in Figure 3.

Figure 3:  Trade Winds and Averaged East Coast Sea Levels

Across the north of Australia, the match is close and strong.

Figure 4:  Trade Winds and North Australian Sea Levels

Figure 5 shows the average of the tide gauges, including Cocos Island, far out in the Indian Ocean.

Figure 5:  Trade Winds and Average North Australian Sea Levels

Figure 6:  Trade Winds and Average North Australian Sea Levels Excluding Cocos Island

The surprise is that the same effect is seen across southern Australian ports, with the TWI scaled down by 30.

Figure 7:  Trade Winds and Southern Australian Sea Levels

Figure 8:  Trade Winds and Average Southern Australian Sea Levels

When the trades are weak, sea level is lower, and vice versa, with a delay of one or two months.  The trade winds have become stronger over the last 40 years, and sea levels have increased.

Across southern Australia the intensity of high pressure systems has also increased:

Figure 9:  Strength of southern high pressure systems

The strength of high pressure systems in the sub-tropical ridge has increased.  On the southern side blow the Roaring Forties, and on the northern side the South-East Trades.  Stronger winds in the Pacific roughly match stronger winds in the Southern Ocean, pushing the sea up against the coastlines in the north and south.

It could be that stronger circulation is a symptom of global warming (which you may remember I don’t doubt, just the amount and cause).   However water finds its own level.   Sea level rise at Australian ports and some Pacific islands that has been caused by wind-driven water movement has to be matched by sea level fall across broad areas elsewhere.  That’s why coastal tide gauges are not good at measuring global sea level.

There’s more to sea level than you might think.