Mysterious Jump in Ocean Temperatures

May 31, 2020

Back in 2018 Jo Nova publicised Dr John McLean’s exposé of the many ridiculous flaws in HadCruT4, the global temperature dataset, which included until a year ago the oceanic component, HadSST3. That was bad enough, with some data from positions 100km inland from the nearest sea. But in June 2019 the long awaited HadSST4 was released, in which many corrections were made to reduce “problems” in the sea surface temperature record.


Corrections indeed.


Figure 1 is a comparison of HadSST4 with HadSST3.

Figure 1: HadSST3 and HadSST4 since 1850

And figure 2 shows the extent of the “corrections”.

Figure 2: Adjustments: HadSST4 minus HadSST3

You will no doubt note how the “corrections” have made the past cooler, as is standard practice for all those curating temperature records. Indeed, apart from a small foray in the 1940s, the whole 100 years from 1875 to about 1975 has been made ever so slightly- up to a tenth of a degree- cooler.


But in an interesting move, all temperatures since then have been corrected, and, would you believe, upwards. Who would have thought that the average sea surface temperature measured just a couple of years ago in September 2017 was 0.1875 degrees too cool, and needed revising upwards?

Figure 3: HadSST3 and HadSST4 since 2010

Modern thermometers just aren’t what they used to be.

CO2vid Watch: April

May 7, 2020

In my last post I wondered whether the largest real-life science experiment in history will show whether atmospheric carbon dioxide concentrations will decrease as a result of the Covid19-induced economic slowdown.

I concluded:  I expect there may be a small decrease in the rate of CO2 concentration increase, but it won’t be much, and I will be surprised if it turns negative.  A large La Nina later this year will lead to a CO2 increase a few months later, in which case there will be a larger downturn in annual CO2 change in 2021.

However, if the major cause of CO2 increase is fossil fuel consumption, there will be an extra large decrease in CO2 change in 2020 and 2021- and a noticeable jump if the global economy rebounds.”

 Figure 1 shows the 12 month change in CO2 at Mauna Loa since 2015-that is, January to January, February to February, March to March (as in Figure 6 of my previous post):

Fig. 1:  12 month change in CO2 concentration since 2015- Mauna Loa

The CO2 concentration number for April is now published: 416.21 p.p.m. (parts per million).  That’s an increase of 1.71 ppm over the March figure, and 2.89 ppm above the figure for April last year.  Figure 2 is the April update on Figure 1.

Fig. 2:  Updated 12 month change in CO2 concentration since 2015- Mauna Loa

Notice the amount of 12 month change has increased, despite at least two months of downturn in China and at least a month in most other countries.

Figure 3 is a monthly update for 2020 I will show as each month’s CO2 figures become available (and 2021 if necessary):

Fig. 3:  Updated 12 month changes in CO2 concentration for 2020- Mauna Loa

Figure 4 shows the range of 12 month changes for each decade since the record began in 1958:

Fig. 4:  Updated 12 month changes in CO2 concentration all decades- Mauna Loa

Figure 5 shows the same, but just since 2000:

Fig. 5:  Updated 12 month changes in CO2 concentration since 2000- Mauna Loa

Note that so far this year, 12 month changes are in the upper range, and there is no sign of any slow down.

Watch for next month’s update, and enjoy the ride!

Will Covid-19 Affect Carbon Dioxide Levels?

May 2, 2020

The Coronavirus pandemic has already caused a huge downturn in many industries world-wide- especially tourism, manufacturing, and transport.  Prices of oil and thermal coal have fallen dramatically.  The first impact was on China, as this plot from the World Economic Forum shows:

Fig. 1:  Industrial production in China

Industrial production has fallen by 13.5% in January and February, and exports have dropped by 17%.  While China may be recovering from the virus, the rest of the world is not and knock-on effects from low Chinese production of essential inputs will hold back recovery in other countries.

So the question is: if atmospheric concentrations of carbon dioxide and other greenhouse gases are largely a product of fossil fuel emissions, and if fossil fuel emissions decrease, will we see a reduction in the rate of increase of CO2, and if so, how much?

This is the biggest real life experiment we are ever (I hope) likely to see.

Background:

The concentration of CO2 in the atmosphere is increasing, as in Figure 2.

Fig. 2:  CO2 measurements at Mauna Loa

Cape Grim in Tasmania samples the atmosphere above the Southern Ocean and shows a similar trend, with much smaller seasonal fluctuations:

Fig. 3:  CO2 measurements at Cape Grim

But what we are vitally interested in, is how much we may expect CO2 concentration to change.  We can show change, and remove the seasonal signal, by plotting the 12 month differences, i.e., March 2020 minus March 2019.  Thus we can see how much real variation there is even without an economic downturn.  And it is huge.

Fig.4:  12 month change in CO2 concentration- Mauna Loa

Fig. 5:  12 month change in CO2 concentration- Cape Grim

Not very much smaller at Cape Grim.

However, the Mauna Loa record is the one commonly referred to.  Figure 6 shows the 12 month changes since 2015.

Fig.6:  12 month change in CO2 concentration since 2015- Mauna Loa

We will keenly watch the values for the remaining months of 2020, and then 2021.

My expectation?

I will be very surprised if there is much visible difference from previous years at all, as the following plots show.  Figure 7 shows the time series of annual global CO2 emissions and scaled up atmospheric concentration from 1965 to 2018 (the most recent data from the World Bank):

Fig. 7:  Carbon Dioxide Emissions and Concentration to 2018

Fig. 8:  Carbon Dioxide Emissions as a Function of Energy Consumption to 2018

There is a very close match between emissions and energy consumption of all types- including nuclear, hydro, and renewables.

Fig. 9:  CO2 Concentration as a Function of Carbon Dioxide Emissions to 2018

Again, it is close, they are both increasing, but with some interesting little hiccups….

So what is the relationship between change in atmospheric concentration and change in emissions?

Fig. 10:  Percentage Change in CO2 Concentration as a Function of Percentage Change in Carbon Dioxide Emissions to 2018

Not very good correlation: 0.01.

Fig. 11:  Percentage Change in Energy Use, GDP, and Carbon Dioxide Emissions to 2018

GDP fluctuates much more than energy or emissions, which are very close, and if anything tends to follow them.

Figure 12 is a time series of annual percentage change in energy and emissions and absolute change in CO2 concentration.

Fig. 12:  Percentage Change in Energy Use and Carbon Dioxide Emissions and Absolute CO2 Change to 2018

You will note that during the three occasions (1974, 1980-82, and 2008-09) when global emissions growth went negative (as much as minus two percent), CO2 concentration barely moved, and still remained positive, and on two occasions when CO2 concentration increased by 3 ppm or more (1998 and 2016), emissions increase was much reduced. 

Ah-ha, but that’s because the volume of the atmosphere is so huge compared with the amount of greenhouse gases being pumped out- according to the Global Warming Enthusiasts.

In Figure 10 I showed that there was little relationship between annual change in CO2 emissions and atmospheric concentration.  Figure 13 shows what appears to have a much greater influence on CO2 concentrations: ocean surface temperature. 

Fig. 13:  Annual Change in CO2 Concentration as a Function of Change in Sea Surface Temperature (lagged 1 year)

Remember the correlation of CO2 with emissions in Figure 10 was 0.01.  The correlation between CO2 and lagged SSTs is 0.59.  That’s a pretty devastating comparison.

Figure 14 shows how in most years SST change precedes CO2 change throughout the entire CO2 record.

Fig. 14:  Annual Change in CO2 Concentration and Sea Surface Temperatures

There is little evidence for CO2 increase causing SST increase, while there is evidence that SST change (or something closely associated with it) leads to CO2 change.   The largest changes coincide with large ENSO events.

Conclusion:

Therefore, I expect there may be a small decrease in the rate of CO2 concentration increase, but it won’t be much, and I will be surprised if it turns negative.  A large La Nina later this year will lead to a CO2 increase a few months later, in which case there will be a larger downturn in annual CO2 change in 2021.

However, if the major cause of CO2 increase is fossil fuel consumption, there will be an extra large decrease in CO2 change in 2020 and 2021- and a noticeable jump if the global economy rebounds.

As I said, a very large real life experiment. So watch this space!

Australia’s Wacky Weather Station Comparison 4: Penrith (NSW)

February 20, 2020

After surveying 666 weather stations across Australia and finding nearly half (49.25%) are not compliant with Bureau of Meteorology siting specifications, in this series of posts I compare daily temperature data from pairs of compliant and non-compliant stations. Here’s the first in this series.

Penrith and Richmond RAAF

These stations are in western Sydney, 16km apart.

Fig. 1:  Penrith map location per Google Maps

Fig.2:  Penrith and Richmond

Penrith Lakes AWS 67113 is beside a large area of excavation and bare soil, and 200 metres from a large artificial lake.

Fig. 3:  Penrith (Google satellite image 2019)

Richmond RAAF 67105 is at an Air Force base. It is open, flat, and at least 50 meters from any concrete or tarmac.

Fig. 4:  Richmond RAAF site plan 2016

Fig. 5:  Richmond RAAF (Google satellite image 2020)

Richmond is 6 metres higher than Penrith.  Both are Automatic Weather Stations with electronic temperature probes transmitting data every minute. While there can be no observer error, as we shall see there can be instrumental error.

Richmond RAAF is an ACORN station. The Bureau says in its Station Catalogue: “The region is a major growth corridor for Sydney and there is evidence of anomalous warming of minimum temperatures in recent years.”

If we plot all daily maxima from 2010 to 2019 for Richmond against Penrith, we see that temperatures match quite closely:

Fig. 6:  Tmax at Richmond as a function of Tmax at Penrith

Richmond is on average cooler than Penrith. A time series of the 31 day centred mean of the daily difference between them shows more detail:

Fig. 7:  31 day mean daily difference Penrith minus Richmond Tmax

Values above zero mean Penrith is warmer than Richmond; below zero, Penrith is cooler.  Most summers Penrith is warmer, and winters slightly cooler, though the record appears to have breakpoints in early 2012 and early 2016, and some unusually high values.

This is a plot of mean differences by month:

Fig. 8: 31 day mean daily difference Penrith minus Richmond Tmax by month

Penrith is warmer in every month, especially in summer, though there are some cooler values in every month.

Minimum temperatures at Richmond are much cooler than Penrith:

Fig. 9:  Tmin at Richmond as a function of Tmin at Penrith

Fig. 10:  31 day mean daily difference Penrith minus Eichmond Tmin

Penrith is 2C to 2.5C warmer in cooler months and up to 0.5C warmer in summer.

Fig. 11: 31 day mean daily difference Penrith minus Richmond Tmin by month

A note on accuracy:

The centred 31 day running correlation is useful for detecting inconsistencies.

Fig. 12:  Centred  31 day running correlation between Penrith and Richmond maxima

Fig. 13:  Centred  31 day running correlation between Penrith and Richmond minima

The much poorer correlation in the summer of 2013-2014 shows in Figures 7 and 10. Here are the actual minimum temperatures recorded:

Fig. 14:  Daily minima at Penrith and Richmond Summer 2013 – 2014

It appears that the Richmond probe began malfunctioning in mid-December and failed completely in mid-January. It failed again a few months later.

In recent years, Penrith Lakes AWS 67113 has recorded generally warmer maxima than Richmond RAAF 67105 in summer and comparable or slightly cooler maxima in winters. Minima are always much warmer at Penrith. This may be due to the proximity to the large artificial lake.

In this example, siting non-compliance has a large effect on temperature.

***

This will be the last comparison, as it is very difficult to identify non-compliant stations with nearby compliant sites with similar environment. We can conclude however that non-compliance with siting specifications affects temperatures recorded, which varies between locations. Sometimes maxima are much warmer, sometimes minima. Temperatures at 329 non-compliant stations cannot be regarded as reliable for weather or climate analysis.

Australia’s Wacky Weather Station Comparison 3: Echuca (Vic)

February 18, 2020

UPDATE 20/02/2020: As reader Phil has reminded me and as I said after Figure 5 below, Kyabram appears to be irrigated and so should be added to the non-compliant list (making 329 or 49.25% of checkable stations). Therefore these sites are not suitable for comparison as factors other than siting (e.g. cooling due to evapo-transpiration following irrigation) will affect temperature difference. It is very difficult to find compliant sites that are near enough to non-compiant stations- but these are still interesting sites.

After surveying 666 weather stations across Australia and finding nearly half (49.25%) are not compliant with Bureau of Meteorology siting specifications, in this series of posts I compare daily temperature data from pairs of compliant and non-compliant stations. Here’s the first in this series.

Echuca and Kyabram

These stations are about 170km north of Melbourne, about 33km apart.

Fig. 1:  Echuca map location per Google Maps

Fig.2:  Echuca and Kyabram

Echuca Airport 80015 is right beside a large gravel parking area and less than 40 metres from the tarmac aircraft parking area.

Fig. 3:  Echuca Airport (Google satellite image 2019)

EchucaAir aerial

Kyabram 80091 is at a former research station in an open paddock as the 2008 plan shows:

Fig. 4:  Kyabram site plan 2008

Fig. 5:  Kyabram (Google satellite image 2020)

Kyabram is 9 metres higher than Echuca.  Again, an important difference is that Echuca is a manual station with liquid-in-glass thermometers, while Kyabram is an Automatic Weather Station (installed 1998) with an electronic temperature probe transmitting data every minute. The satellite image shows the enclosure is not well maintained with what appears to be long grass. The area around the enclosure is probably irrigated so this station should probably be classified as non-compliant as well.

If we plot all daily maxima from 2010 to 2019 for Kyabram against Echuca, we see that temperatures match quite closely:

Fig. 6:  Tmax at Kyabram as a function of Tmax at Echuca

The trend equation shows Kyabram is on average cooler than Echuca. A time series of the 31 day centred mean of the daily difference between them shows more detail:

Fig. 7:  31 day mean daily difference Echuca minus Kyabram Tmax

Values above zero mean Echuca is warmer than Kyabram; below zero, Echuca is cooler.  Note that apart from a few brief episodes, Echuca is always warmer than Kyabram.

This is a plot of mean differences by month:

Fig. 8: 31 day mean daily difference Echuca minus Kyabram Tmax by month

Echuca is warmer in every month- apart from those brief periods shown in Figure 7.

Minimum temperatures don’t match as closely…

Fig. 9:  Tmin at Kyabram as a function of Tmin at Echuca

Fig. 10:  31 day mean daily difference Echuca minus Kyabram Tmin

Echuca is generally warmer. There are several examples of odd deviations.

Fig. 11: 31 day mean daily difference Echuca minus Kyabram Tmin by month

A note on accuracy:

The centred 31 day running correlation is useful for detecting inconsistencies.

Fig. 12:  Centred  31 day running correlation between Echuca and Kyabram maxima

Fig. 13:  Centred  31 day running correlation between Echuca and Kyabram minima

The weaker correlation in 2011 is coincident with the unusual difference as seen in Figure 10 and is worth a closer look.

Fig. 14:  Daily minima at Echuca and Kyabram Winter 2011

Here we see probable examples of temperatures being recorded on the wrong date.

In recent years, Echuca 80015, a manual station that does not comply with site specifications, has warmer maxima than its neighbour Kyabram 80091 except for brief episodes, and mostly warmer minima.

In this example, siting non-compliance has a large effect on temperature, but may affect both sites.

Australia’s Wacky Weather Station Comparison 2: Wagin (WA)

February 16, 2020

After surveying 666 weather stations across Australia and finding nearly half (49.25%) are not compliant with Bureau of Meteorology siting specifications, in this series of posts I compare daily temperature data from pairs of compliant and non-compliant stations. Here’s the first in this series.

Wagin and Katanning

These stations are about 200km south-east of Perth.

Fig. 1:  Wagin map location per Google Maps

Katanning is in a paddock 48.7km south-east of Wagin.

Fig.2:  Wagin and Katanning

Wagin 10647 is in the middle of a small town. The screen has a bare dirt path leading to it. It is 10 metres from a bitumen street. A colourbond fence is to the north-east and an 18 metre tree is less than 20 metres away. More trees are to the south.

Fig. 3:  Wagin (Google satellite image 2019)

Katanning 10916 is in an open rural setting, on a slope as the 2013 site plan shows:

Fig. 4:  Katanning site plan 2013

Fig. 5:  Katanning (Google satellite image 2020)

Katanning is 64 metres higher than Wagin, but the surrounding country is similar- dry, flat or gently sloping.  Again, an important difference is that Wagin is a manual station with liquid-in-glass thermometers, while Katanning is an Automatic Weather Station (installed 1998) with an electronic temperature probe transmitting data every minute.

If we plot all daily maxima from 2010 to 2019 for Katanning against Wagin, we see that temperatures match quite closely:

Fig. 6:  Tmax at Katanning as a function of Tmax at Wagin

The trend equation shows Katanning is on average more than 0.5C cooler than Wagin. A time series of the 31 day centred mean of the daily difference between them shows more detail:

Fig. 7:  31 day mean daily difference Wagin minus Katanning Tmax

Values above zero mean Wagin is warmer than Katanning; below zero, Wagin is cooler.  Note that apart from a brief episode in 2012, Wagin is always warmer than Katanning.

This is a plot of mean differences by month:

Fig. 8: 31 day mean daily difference Wagin minus Katanning Tmax by month

Wagin is warmer in every month- apart from a three month period in 2012 which shows in the black ellipse.

Minimum temperatures don’t match as closely…

Fig. 9:  Tmin at Katanning as a function of Tmin at Wagin

Fig. 10:  31 day mean daily difference Wagin minus Katanning Tmin

Wagin is warmer in summer but cooler in winter. Possibly, due to the sloping ground at Katanning, cold air flows downhill away from the screen in cool months, keeping recorded minima higher than in Wagin.

Fig. 11: 31 day mean daily difference Wagin minus Katanning Tmin by month

A note on accuracy:

The centred 31 day running correlation is useful for detecting inconsistencies.

Fig. 12:  Centred  31 day running correlation between Wagin and Katanning maxima

Fig. 13:  Centred  31 day running correlation between Wagin and Katanning minima

Although there are a couple of obvious inconsistencies in maxima, the correlation in minima has been much worse every year.

Fig. 14:  Daily difference in maxima

There are examples of up to 6 degrees difference on some days, and some much larger, possibly due to observation or recording error- for example, by recording temperature on the wrong day, or recording 19.6 instead of 29.6.

In recent years, Wagin 10647, a manual station that does not comply with site specifications, has warmer maxima than its compliant neighbour Katanning 10916 all year round, and has warmer minima in summers. 

In this example, siting non-compliance has a large effect on temperature.

Australia’s Wacky Weather Station Comparison 1: Keith (SA)

February 15, 2020

After surveying 666 weather stations across Australia and finding nearly half (49.25%) are not compliant with Bureau of Meteorology siting specifications, in this series of posts I compare daily temperature data from pairs of compliant and non-compliant stations.

The difficulty is to find pairs of sites in close physical proximity and similar surroundings.  Large numbers of non-compliant stations especially in WA and SA have no compliant neighbours. 

Another difficulty is that it is impossible to control variables other than siting.  Screen maintenance, enclosure maintenance, probe or thermometer accuracy, are some of the variables that may adversely affect comparisons.  Never-the-less, we shall try.

I have restricted analysis to the last 10 years (2010 – 2019).

Keith and Munkora

These stations are in the far south-east of South Australia, not far from the Victorian border:

Fig. 1:  Keith map location per Google Maps

 They form the closest pair of stations I have found, just 2.66 kilometres apart, as this map shows.

Fig.2:  Keith and Munkora (Keith West)

Keith 25507 is in the middle of town between the highway and the rail line between Adelaide and Melbourne.

Fig. 3:  Keith (Google satellite image 2019)

Munkora 25557 is in an open rural setting, but is really “marginal” rather than compliant, as the grass in the enclosure is up to 0.5m high, and the surrounding paddock has at times been a cultivation, as the 2017 site plan shows:

Fig. 4:  Munkora site plan 2017

Still, it’s better than Keith.

Fig. 5:  Munkora  (Google satellite image 2020)

There is only 2 metres difference in altidude.  However, an important difference is that Keith is a manual station with liquid-in-glass thermometers, while Munkora is an Automatic Weather Station (installed 2001) with an electronic temperature probe transmitting data every minute.

If we plot all daily maxima from 2010 to 2019 for Munkora against Keith, we see that temperatures match quite closely:

Fig. 6:  Tmax at Munkora as a function of Tmax at Keith

A time series of the 31 day centred mean of the daily difference between them shows more detail:

Fig. 7:  31 day mean daily difference Keith minus Munkora Tmax

Values above zero mean Keith is warmer than Munkora; below zero, Keith is cooler.  Note that Keith is warmer in cooler months but Munkora is warmer in summer and autumn.  Note also strange things happen in the summers of 2010-2011, 2014-2015, and 2015-2016.

This is a plot of mean differences by month:

Fig. 8: 31 day mean daily difference Keith minus Munkora Tmax by month

Keith is warmer in cool months (May to September).  However, the warmer maxima at Munkora in warmer months may be due to the rapid response of the AWS probe to sudden temperature changes which an LIG maximum thermometer will not detect.  The BOM denies this happens.

Minimum temperatures don’t match as closely…

Fig. 9:  Tmin at Munkora as a function of Tmin at Keith

But minima at Keith are consistently warmer (averaging about 1.5 degrees C) than 2.7km out of town, and the differences are much greater:

Fig. 10:  31 day mean daily difference Keith minus Munkora Tmin

Keith is warmer in all seasons, especially spring and summer.

Fig. 11: 31 day mean daily difference Keith minus Munkora Tmin by month

A note on accuracy:

The centred 31 day running correlation is useful for detecting inconsistencies.

Fig. 12:  Centred  31 day running correlation between Keith and Munkora maxima

Fig. 13:  Centred  31 day running correlation between Keith and Munkora minima

Although there are a couple of obvious inconsistencies in maxima, the correlation in minima has been getting worse over the years and was much worse in 2019.

Fig. 14:  Daily difference in minima

There are examples of up to 10 degrees difference on some days, possibly due to observation or recording error- for example, by recording temperature on the wrong day.

In recent years, Keith 25507, a manual station that does not comply with site specifications, has warmer winter maxima but cooler summer maxima than the AWS at Munkora 25557 just 2.66km out of town, and has warmer minima all year round. 

Keith, with a population of just over 1,000, appears to have an Urban Heat Island (UHI) effect, due to its poor siting.

In this example, siting non-compliance has a large effect on temperature.

Downwelling Infra-Red Radiation and Temperature: Part 2

February 7, 2020

In Part 1 I showed that:

  • Downwelling infra-red radiation (so called “back radiation”) is real and measurable including at night.
  • It is greatly increased by cloud and humidity,
  • It results from daytime heating of the ground, which then loses heat by conduction, convection, evaporation, and radiation, into the atmosphere where the IR is repeatedly absorbed and re-emitted in all directions by greenhouse gases (including water vapour).
  • A warmer atmosphere from whatever cause, natural or enhanced, will result in greater downwelling IR.

In this post I will look at the relationship between downwelling IR and temperatures at five Australian locations during 2018 (the last year for which complete irradiance data is available.)  Those locations are Alice Springs, Darwin, Rockhampton, Melbourne, and Cape Grim, and are shown on this map.

Fig.1:  Australian stations with solar exposure data

Cape Grim, set on a clifftop above the Southern Ocean, is most exposed to marine influences.  Melbourne, Rockhampton, and Darwin are surrounded by land but are subject to marine influence at times when the wind blows from the ocean.  Alice Springs has a desert climate and the ocean is thousands of kilometres away.  Most examples in this post will come from the Alice.

The Relationship Between Maxima and Minima:

Consider this plot of temperature at Walgett (NSW):

Fig. 2:  Latest weather graph for Walgett 27 – 31 January 2018

During a fine clear day the sun heats the ground which by conduction and convection raises the near-surface air temperature.  The hot ground emits upwelling IR, some of which greenhouse gases in the atmosphere absorb and re-emit in all directions, including towards the earth.  This is downwelling IR (DWIR), which adds to the solar radiation during the day, and slows the loss of heat at night.  The air temperature, and DWIR, peaks usually in the mid to late afternoon.  As the ground cools slowly throughout the evening and night hours, IR continues to be exchanged upwards and downwards, with enough being lost to space for ground and air temperatures to cool to the minimum.  This is usually reached, in fine clear conditions, sometime after sunrise.  And that is usually the time when DWIR also reaches minimum values.

Before I look at the relationship between DWIR and minima, let’s look at plots of maxima and minima.

Fig, 3:  Maxima and Minima at Alice Springs during 2018:

Note that usually (but not always!) peaks in maxima are matched by peaks in minima.  Here’s a closer look at the period from 6 May to 20 July, with minima scaled up by 19 degrees:

Fig. 4:  Maxima and Scaled Minima, 6 May – 20 July 2018

Note that maxima highs and lows precede those of minima by one day NEARLY ALWAYS.  (Sometimes they occur together, and sometimes maxima precedes minima by two days.)  The minimum temperature reflects the previous day’s maximum.  Why?  Due to DWIR, the ground cools slowly.  A hot day generates lots of DWIR, which keeps the ground (and air temperature) warmer next morning.  A cool day means less DWIR available next morning.  However, clouds lower maxima by reflecting sunlight but increase DWIR to keep nights and minima warmer, as we shall see later. The pattern seen above is also seen at Cape Grim, Melbourne, and Rockhampton, but not in Darwin where it is not so clear at all.

The Relationship Between Downwelling IR and Minima:

I used solar irradiance data to find daily (to 9.00 a.m.) minimum DWIR values for 2018 at Alice Springs, Darwin, Rockhampton, Melbourne, and Cape Grim, for comparison with daily temperature minima. 

Fig. 5:  Daily minima for 2018 at all stations

Fig. 6:  Daily minimum DWIR for 2018 at all stations

At all sites, as daily minimum IR increases, daily minimum temperature increases.  However, the strength of the relationship varies.  I calculated derivatives of Tmin and IR to find the daily change in values.  The relationship is strongest at Alice Springs, with a correlation of 0.69, Figure 5:

Fig. 7:  Change in temperature as a function of change in DWIR at Alice Springs.

Melbourne has almost exactly the same correlation (0.68), followed by Cape Grim (0.64) and Rockhampton at 0.61.  However Darwin is much different:

Fig. 8:  Change in temperature as a function of change in DWIR at Darwin.

The reason for this is not as complex as I thought, but first I’ll show a method of showing (and testing) the relationship between DWIR and Tmin more easily.

Converting DWIR to Representative Atmospheric Temperature

From the Bureau’s solar radiation glossary, http://reg.bom.gov.au/climate/austmaps/solar-radiation-glossary.shtml#globalexposure :

Downward infra-red irradianceis related to a `representative (or effective radiative) temperature’ of the Earth’s atmosphere by the Stefan-Boltzmann Law:

E = σ T4

Where: E = irradiance measured [W/m2]
σ = Stefan-Boltzmann constant [5.67 x 10-8 W/m2/K4
T = representative atmospheric temperature [K]

From this we can calculate the daily Representative Atmospheric Temperature (RAT) above each weather station.  Here is a plot of RAT for Alice Springs.

Fig. 9: Representative Atmospheric Temperature and Minima at Alice Springs

RAT is always colder than the surface.  Notice how closely Tmin tracks with RAT. 

To compare them more closely, I scaled up RAT by adding the average monthly difference from Tmin.  Now you can see how closely minimum temperature is related to RAT and thus DWIR.

Fig. 10:  Scaled Representative Atmospheric Temperature and Minima at Alice Springs

Zooming in to the period from 31 March to 4 June:

Fig. 11 :  Scaled RAT and Minima at Alice Springs, 31 March – 4 June 2018

The timing of variations is very close.

Here is a plot of the actual daily difference between minimum surface temperature and Representative Atmospheric Temperature.  I have marked some unusually low and high values for closer inspection..

Fig. 12:  Daily difference between Surface Minima and RATat Alice Springs

What causes these fluctuations?  Returning to actual temperature and calculated RAT, here is the plot for the year to 15 April:

Fig. 13:  RAT and Minima at Alice Springs, 1 January – 15 April 2018

Both Tmin and RAT usually move in unison, rising and falling together.  However, notice at point A there is very little difference between the values, but at point B there is a very large difference.

Here’s the plot for November and December.  A and B have very small differences, while C and D have very large differences.

Fig. 14:  RAT and Minima at Alice Springs, 6 November – 31 December 2018

Cloudy conditions increase downwelling IR.  With no daily cloud data, rainfall will be a proxy for some cloudy days.  (There will be plenty of cloudy days when there is no rain.)  Here is a plot of rainfall and the difference between surface minima and calculated RAT.

Fig. 15:  Rainy weather and Tmin minus RAT at Alice Springs

Rainfall appears to coincide with very low differences when RAT (derived from DWIR) has increased but corresponding Tmin has not increased as much as expected.  Let’s zoom in to look at Points A and B from Figure 13 above.

Fig. 16:  Rainy weather and Tmin minus RAT at Alice Springs, January – April

In fact rain coincides with nearly all of the low differences.  Point B remains anomalously high.  What about November and December?

Fig. 17:  Rainy weather and Tmin minus RAT at Alice Springs, November – December

Here we have a problem.  Points A and B from Figure 14 above line up with rain events.  Instead of being a low difference as expected, point C has a high value coinciding with a small rain event, and D is on its own.  Why?

When RAT is scaled up, the problem (and likely reason) is obvious:

Fig. 18  Scaled RAT and Minima at Alice Springs, December 2018

No IR data is recorded for 11 December.  I suspect that IR values should also be missing for 12 and 13 December.  Moving remaining data for the month two days later removes these strange inconsistencies (and also dramatically improves correlation between IR change and temperature change to above 0.7.)

Which still leaves the odd spike in Figure 13 at point B.

The Exception Proves The Rule

Here is a count of the number of days with no IR data at Alice Springs in 2018.

Fig.19:  Count of days with no data at Alice Springs

There are a few minutes of missing data on nearly every day, but data was completely absent for eight whole days in March, and three days in December.  Did the pyrgeometer stop recording suddenly?  Was it a sudden fault or was it failing gradually?  Figure 20 shows the 31 day centred running correlation between change in DWIR and change in Tmin, with missing days shown.

Fig. 20:  Centred 31 day running correlation between change in DWIR and change in Minima

If all is well, and the relationship between change in DWIR and temperature minima is sound, the correlation between them should be fairly constant.  However, if the pyrgeometer reads incorrectly (or else the temperature probe- another possibility, but not in this case), correlation will suffer.  This is shown in March and December.  From April to September, change in Tmin correlates well with change in DWIR being between 0.8 and 0.9 for nearly the whole time.

Now let’s look at Darwin, which we saw in Figure 8 above was poorly correlated.   The running correlation shows when faults may have occurred.

Fig. 21:  Centred 31 day running correlation between change in DWIR and change in Minima

The dips above coincide with equipment failure in January, March, November and December.  There also appears to be a problem in August – September.

It does not help that the equipment failures occur in rainy, cloudy periods (Wet and Build-up).

Fig. 22:  Rainy weather and Tmin minus RAT at Darwin

In the Dry, with no rain, the difference between Tmin and the RAT (Representative Atmospheric Temperature) still fluctuates wildly.  Here is a plot of the difference for June 2018:

Fig. 23:  Daily difference between Surface Minima and RATat Darwin June 2018

If the relationship is valid, and there are no recording problems, then large differences occur during fine and cloudless conditions and low values indicate cloudy conditions.  The daily total of Global Solar Exposure can also be a metric of cloudiness, because smaller amounts of sunlight reach the ground on cloudy days.   Figure 24 is a plot of the sum total of Global Irradiance in kiloWattminutes per square metre received each day.

Fig. 24: Daily total of Global Irradiance Darwin, June 2018

Apart from 10 – 12 June, the relationship holds.  Darwin’s apparent poor relationship between DWIR and Minima is very probably due to equipment failure.

The apparent exceptions to the “rule” that large differences between minima and Representative Atmospheric Temperature occur in dry, cloud free conditions, and small differences in cloudy conditions, in fact confirm it. 

Conclusion:

  • Downwelling infra-red radiation (so called “back radiation”) is real and measurable including at night.
  • It is greatly increased by cloud and humidity.
  • It results from daytime heating of the ground, which then loses heat by conduction, convection, evaporation, and radiation, into the atmosphere where the IR is repeatedly absorbed and re-emitted in all directions by greenhouse gases (including water vapour).
  • A warmer atmosphere from whatever cause, natural or enhanced, will result in greater downwelling IR.
  • Temperature Maxima highs and lows precede those of minima by one day NEARLY ALWAYS, due to the influence of downwelling IR.
  • Calculating Representative Atmospheric Temperature from downwelling IR using the  Stefan-Boltzman Law provides further insights.
  • The daily minimum RAT is always much colder than minimum temperature.
  • The difference between the two changes with the weather.  Sunny, dry, cloudless weather is associated with large differences, while cloudy weather is associated with small differences.
  • When recording error is accounted for there is very good correlation between downwelling infra-red irradiance and daily minimum temperatures at a range of sites across Australia.
  • In Australia, meteorological equipment can deteriorate for some time and fail completely, resulting in faulty data being included in national databases.
  • Finally, the effect of DWIR on minima is not site dependent.  Both Melbourne and Rockhampton have Urban Heat Island influence but the relationship is similar to that of other sites.  Minima are directly related to DWIR, but DWIR is increased not only by clouds, but also by large trees, nearby buildings, and areas of concrete and bitumen.

BBC Accused of Misleading Reporting About Melting Antarctic Glacier

January 30, 2020

Every morning I get these annoying “click bait” pop-ups on my phone, which I usually ignore. This morning I weakened, and tapped the headline:

Antarctica Melting: Climate change and the journey to the “doomsday glacier”.

Knowing a bit about Antarctica, I dismissed it as more BBC rubbish, but just a few minutes ago I received a message from the Institute of Public Affairs with a link to a press release and article by the Global Warming Policy Forum. Here it is in full:

Press Release 29/01/20
 
BBC Accused of Misleading Reporting About Melting Antarctic Glacier
 
Why did the BBC fail to mention the volcanoes underneath?

London, 29 January: The Global Warming Policy Forum has criticised the BBC for misleading the public about the melting of the Thwaites Glacier.
 
In its numerous reports online, on radio and on television, the BBC blamed the melting of this Antarctic glacier on climate change. However, the BBC’s reports do not mention an important fact that has been widely known and that the BBC itself has reported previously – the influence of volcanoes beneath the glacier.
 
Scientists have known for years that subglacial volcanoes and other geothermal “hotspots” underneath the glacier are contributing to the melting of the Thwaites Glacier.

“Despite claims about climate change and admonition to lower our greenhouse gas emission as a way to ameliorate the melting of Thwaites, the BBC should have been pointing out that what is happening underneath the glacier could be in large parts an act of geology and one of those natural and globally-important dynamics that have been occurring throughout the ages,” said GWPF science editor Dr David Whitehouse.

What is more, the scientists will remain on Thwaites for a while. They have not analysed their data yet, so claims that they have confirmed “the Thwaites glacier is melting even faster than scientists thought…” are premature.

…..

More information about the Thwaites Glacier and the BBC’s misleading reporting can be found on the GWPF website.

I have long suspected that any warming in Antarctica might be due to the large volcanic province beneath West Antarctica, when UAH satellite temperatures show no sign of Antarctic warming, as I have shown here.

I’m pleased the GWPF is onto it so quickly, and many thanks to the IPA for alerting me.

Downwelling Infra-Red Radiation and Temperature: Part 1

January 22, 2020

Way back in July last year I posted about the long term decrease in downwelling IR at Cape Grim and Alice Springs, despite rising CO2.

From the Bureau’s solar radiation glossary,
“Downward infrared irradiance is a measurement of the irradiance arriving on a horizontal plane at the Earth’s surface, for wavelengths in the range 4 – 100 μm (the wavelength emitted by atmospheric gases and aerosols). It is related to a `representative (or effective radiative) temperature’ of the Earth’s atmosphere by the Stefan-Boltzmann Law:
E = σ T4
Where: E = irradiance measured [W/m2]
σ = Stefan-Boltzmann constant [5.67 x 10-8 W/m2/K4
T = representative atmospheric temperature [K]
Consequently, this quantity will continue to have a positive value, even at night time. It can be measured using an Eppley PIR pyrgeometer.”

As atmospheric temperature increases, DWIR must also increase. This would be a symptom of warming.
A reader commented: ”What we need is DWIR nighttime measurements only (preferably without clouds) in a location where there is little or no water vapour. Atacama Chile would be perfect. Alice Springs maybe but less so. i am willing to bet that one couldn’t measure the DWIR at night without clouds in Atacama because it would be so low.”
I am unable to get data for Atacama, but here is DWIR data for Alice Springs for July 2018. July is mid-winter and usually dry and cloud free. No rain fell in July 2018 at the Alice.
Figure 1 shows maxima and minima for the month:
While July had no rain, there were several large weather changes shown by the spikes and dips in temperature. Coldest temperatures were on 12-13-14 July.
Fig.1: Surface temperatures Alice Springs July 2018

Next, downwelling IR. The weather changes show up in IR as well.
Fig.2: Downwelling IR Alice Springs July 2018

Now for IR in the hours of darkness:
Fig.3: Downwelling IR Alice Springs July 2018 at night (6pm to 6am)

Clearly, DWIR is real and measurable at night, in all conditions. It usually (but not always) decreases in a smooth curve. Putting it together, we see a clear daily cycle: DWIR usually increases rapidly in daytime, and decreases at night.
Fig.4: Downwelling IR Alice Springs July 2018 by day and night

Now we look at typical IR behaviour in cool, dry conditions on 12 and 13 July 2018. The x-axis is in 3 hourly divisions and I have marked in midnight of 12-13.
Fig.5: Downwelling IR Alice Springs 12-13 July 2018

Note the curve is not completely smooth: there are little variations due to pockets of different temperatures in the air. The lowest DWIR values (227.36 Watts/sq.metre averaged over one minute) are reached around 8.00 a.m. shortly after sunrise, then values rise rapidly before tapering off to peak in the late afternoon. During the night they decrease until the sun heats the ground again in the morning.
Now for the period 5 to 8 July:
Fig.6: Downwelling IR Alice Springs 5-8 July 2018

On the 6th and 8th strange things happen after midnight, almost certainly clouds.
Strange things also happen from 23 to 25 July. On the 24th a heavy bank of cloud comes over and clears with a sudden dry change after sundown, with more separated clouds arriving later at night before finally clearing about 9 a.m. next morning.
Fig.7: Downwelling IR Alice Springs 23 – 25 July 2018

How do I know those spikes were caused by clouds? Here’s direct radiation and IR for 23-25 July.
Fig.8: Downwelling IR and Direct Irradiance Alice Springs 23 – 25 July 2018

Direct irradiance is the radiation from the sun’s direct beam. It is zero at night but rises rapidly to peak at local solar noon, then rapidly falls to zero at dusk. Not all solar radiation reaches the surface. Some is reflected, some is scattered by dust, smoke, or rain drops, but on a clear day the pattern is like 23 July. On 24 July clouds block the sun’s direct rays for most of the day, and downwelling IR increases markedly. This is from warm moist air in the cloud which has come from somewhere else.
My conclusion:
Downwelling infra-red radiation (so called “back radiation”) is real and measurable including at night.
It is greatly increased by cloud and humidity, and there is always some moisture in the air even in the desert.
It results from the ground heating up in the daytime, which then loses heat by conduction, convection, and radiation, into the atmosphere where the IR is repeatedly absorbed and re-emitted in all directions by greenhouse gases (including water vapour).
A warmer atmosphere from whatever cause, natural or enhanced, will result in greater downwelling IR.


Future posts will look at the relationship between solar radiation, downwelling IR, and temperature.