Global Warming or Global Cooling: Keep an Eye on Greenland

July 30, 2021

Here are four graphs that governments should think about.

The first graph is of ice core temperature data from Vostok in Antarctica for the past 422,000 years.  Temperatures are shown as variation from surface temperature in 1999 of -55.5 degrees Celsius.

(From:- Petit, Jean-Robert; Jouzel, Jean (1999): Vostok ice core deuterium data for 420,000 years. PANGAEA, https://doi.org/10.1594/PANGAEA.55505)

 We are living in an inter-glacial period of unusual warmth, the Holocene, but previous interglacials were 2 to 3 degrees warmer than the present.  Between these brief interglacials are 100,000 year long glacial periods.  As the US National Climatic Data Centre says, “Glacial periods are colder, dustier, and generally drier than interglacial periods.”

We are lucky to be living now- life would be pretty hard for the small population the world could support in a glacial period.

Graph 2 shows just the last 12,000 years.  We are at the extreme right hand end.

Note that Vostok temperatures have fluctuated between +2 and -2 degrees relative to 1999.

There are several ways of identifying the start and end of interglacials.  I have chosen points when Antarctic temperatures first rise above zero and permanently fall below zero relative to 1999.  Graph 3 shows the length of time between these points for the previous three interglacials compared with the Holocene.

The Holocene has lasted longer than the previous three interglacials: and is colder.

Many scientists think glacial periods start when summer insolation at 65 degrees North decreases enough so that winter snowfall is not completely melted and therefore year by year snow accumulates.  Eventually the area of snow (which has a high albedo i.e. reflects a lot of sunlight) is large enough to create a positive feedback, and this area becomes colder and larger.  Ice sheets form, and a glacial period begins.  This is a gradual process that may take hundreds of years.

Well before global temperatures decrease, the first sign of a coming glacial inception will be an increasing area of summer snow in north-eastern Canada, Baffin Island, and Greenland.

I could find no data for northern Canada or Baffin Island, but it is possible to deduce summer snow area for Greenland.

Graph 4 shows the minimum area of snow at the end of summer in Greenland.  (Data from Rutgers University, calculated from North America including Greenland minus North America excluding Greenland.)

The area of unmelted snow at the end of summer in Greenland has grown by about 100,000 square kilometres in the past 30 years.  At this rate Greenland will be completely covered in snow all year round in about 45 years.

Caution: there was no glacial inception in the Little Ice Age- other factors may be involved, cloudiness being one.  Further, a 30 year trend is just weather, and may or may not continue- but with the Holocene already longer and colder than previous interglacials, summer snow cover is one indicator we ignore at our peril.

Cold is not good for life.

How Accurate Is Australia’s Temperature Record? Part 3

March 17, 2021

In previous posts (here and here) I have shown how maximum temperatures (Tmax) as recorded by ACORN-SAT (Australian Climate Observations Reference Network- Surface Air Temperature- ‘Acorn’ for short) have diverged from other measures of climate change, in particular, rainfall.

I’ll continue looking at the Tmax ~ Rainfall relationship, and show how the Bureau of Meteorology (BOM) must never have used it as a quality control measure for temperature recording.  Result: garbage.

Tmax is negatively correlated with rainfall.  Wet years are cooler, dry years are warmer.  If the incoming solar radiation, the landscape and the measuring sites remain the same, over a number of years this physical relationship remains constant.  What is true of rainfall and temperature in my lifetime was also true in my grandfather’s lifetime, and will still be true in my grandchildren’s lifetime.  If the relationship appears to vary, it must be as a result of some other cause, such as:

changes in solar radiation;

changes in the landscape (urban development, tree clearing, irrigation);

changes in the weather station sites (movement to new sites, tree growth, proximity to heat sources,  buildings, or areas of pavement, change of screen size);

changes in measuring equipment or methods (electronic probes instead of mercury in glass, time of observation, recording in Celsius instead of Fahrenheit, millimetres instead of inches); or

changes in the recorded data (wrong dates applied, or adjustments).

Solar exposure has not changed (and it would be a huge problem for Global Warming Enthusiasts and the whole Climate Change industry if it had).  Across Australia, urban development is a minuscule fraction of land area; tree clearing and irrigation have affected a larger area in several regions, but the vast arid interior remains largely unchanged.  We do know however that weather station sites, observation methods, and equipment have changed, and temperature data (and to a much smaller extent, rainfall data) have been “homogenized” in an attempt to correct for these changes.

The 1961 – 1990 Tmax ~ rainfall relationship

The Bureau of Meteorology uses the period from 1961 to 1990 as the baseline for calculating temperature and rainfall means and anomalies.  Figure 1 shows the relationship between Tmax and Rainfall for all years from 1961 to 1990.  I am using BOM data from their Climate Change Time Series page.

Fig. 1:  Annual Tmax plotted against Rainfall, 1961 – 1990

The x-axis represents rainfall, the y-axis represents maximum temperatures.  The trendline is marked, showing Tmax decreases with rainfall. 

In the top left is the trendline label, showing the value for Tmax (y) for the any value of rainfall (x).   I have magnified this in Figure 2.

Fig. 2:  Trendline label, Tmax vs Rain 1961-90

Circled in red is the slope of the trendline (-0.0029:  there is an inverse relationship, with temperature decreasing 0.0029 of a degree Celsius for every extra millimeter of rain). 

Circled in green is the intercept (+29.9476: if rainfall was zero, the trendline would intercept the y-axis at 29.9476 degrees). 

Circled in blue is the R-squared value (0.3744: R^2 indicates how well Tmax and rainfall match, 0 being not at all and 1 being perfectly.  This value indicates a correlation of about -0.61.  Another way of thinking about it is that 37% of Tmax is explained by rainfall.)

Now this is important: the relationship shown in the trendline label should be similar for the whole record, or else something other than the climate has changed.

I will now show how we can use the information in Figure 1 to test the accuracy of Tmax data in three separate ways.

Comparison of Acorn Tmax with theoretical values derived from rainfall.

Using the trendline equation (Tmax = -0.0029 x Rain + 29.9476) we can calculate an estimate of Tmax from rainfall in any given year.  Figure 3 shows the result.

Fig. 3:  1910 – 2020 Acorn Tmax and Theoretical Tmax calculated from rainfall

For most of the 1961-90 period there is a fair match (37%, remember).  Before the mid-1950s Tmax is mostly lower than the rainfall derived estimate, and after 1990 is nearly always very much higher than we would expect for the rainfall. 

A plot of annual differences between Acorn Tmax and Theoretical Tmax (the residuals if you like), the Tmax variation that is not explained by rainfall, shows how much they differ.

Fig. 4:  Annual Differences: Acorn Tmax minus Theoretical Tmax

We would expect some random differences, but not that much and not strongly trending up.

Correlation between Tmax and rain over time

The next figure shows the “goodness of fit” between Tmax and Rainfall from any given year to 2020.  (The plotline stops at 2010 as correlation fluctuates too much with only a few datapoints.)

Fig. 5:  Running R-squared values of Tmax vs Rain for all years to 2010, from any given year to 2020

The relationship plainly changes (and improves) with time. From 1998 to 2020 there is a good correlation between Tmax and rainfall: before this it is woeful. 

The next figure shows running 21 year calculations of R-squared ‘goodness of fit’ between annual maxima and rainfall, and is included for your entertainment. 

Fig. 6:  Centred running 21 year R-squared values of Tmax vs Rainfall

In 1989, the 21 year period from 1979 to 1999 has a correlation between Tmax and Rain of -0.35: less than 12 % of temperature change is explained by rainfall.  20 years later, the value for 1999 to 2019 is -0.9, or 81% of Tmax explained by rainfall.  That amount of difference is farcical.

Moreover, recent data shows a completely different Tmax~Rain relationship from Figure 1. 

Which brings me to the third point.

Change in Tmax ~ Rainfall relationship over time

From the trendline equation in Figure 2, the Tmax ~ Rainfall relationship may be calculated as

(Tmax – intercept)/ Rainfall.   The value for 1961-90 is -0.29C per 100mm.

This plot of the 21 year moving average shows how much this changes.

Fig. 7:  21 Year Centred Running Average of ((Tmax – intercept)/Rainfall) x 100

The expected value of -0.29C/ 100mm is reached from 1973 to 1976, in the middle of the 1961 – 1990 period, as expected.  Based on the 1961-1990 trendline equation, over the last 21 years 100mm of rain reduces Tmax by one tenth of a degree Celsius (and if we extrapolate- not a good idea- that figure will approach zero in about 10 years’ time).   100 years ago that same 100mm of rain would have reduced Tmax by 0.38 degrees.  Why has rain lost its power?

Conclusion

Three plots of the Tmax ~ Rainfall relationship- Figures 4, 5, and 7- show a similar pattern of change in the difference between recorded and theoretical Tmax, the correlation between Tmax and rainfall, and the Tmax ~ Rainfall equation. 

Why has rain lost its power?  It hasn’t- Tmax has become relatively too high.  Historical maximum temperatures as reported in Acorn are not just inaccurate but deeply flawed. 

The Acorn dataset is garbage.

How Accurate Is Australia’s Temperature Record? Part 2

January 19, 2021

In my last post I showed that Australia wide the Tmax ~ rainfall relationship has remained constant for the past 110 years (as it should) but Tmax reported in the Acorn dataset has increased by more than 1.5 degrees Celsius relative to rainfall.  Consequently, the ACORN-SAT temperature dataset is an unreliable record of Australia’s maximum temperatures.

Of course there are other aspects of climate besides rainfall.   In this post I will compare annual ACORN-SAT Tmax data with:

Rainfall

Sea Surface Temperatures (SST)

The Southern Annular Mode (SAM)

Cloudiness

Evaporation

all for the Australian region.

I have sourced all data from the Bureau of Meteorology’s Climate time Series pages

except for SAM data from Marshall, Gareth & National Center for Atmospheric Research Staff (Eds). Last modified 19 Mar 2018. “The Climate Data Guide: Marshall Southern Annular Mode (SAM) Index (Station-based).” 

Tmax, Rainfall, and SST data are from 1910; SAM and Daytime Cloud from 1957, and Pan Evaporation from 1975.  Cloud observations apparently ceased after 2014, and Evaporation after 2017, possibly because of staffing cuts.

Because Pan Evaporation data are only available from 1975 and are reported as anomalies from 1975 to 2004 means, I have recalculated Tmax, Rainfall, SST, SAM, and Daytime Cloud anomalies for the same period so all data are directly comparable.

As in the previous post, I have calculated decadal averages for all indicators to show broad long term climate changes.  Decadal averages show how indicators perform over longer periods.  Each point in the figures below shows the average of the 10 years to that point.  This can then indicate times of sudden shifts or questionable data. (For example in Figure 1 SAM (the green line) makes a sudden jump in 2015.  Was this a climate shift or a data problem?)

Figure 1 shows the 10 year means for all climate indicators.  I have scaled Rain and SST to match Tmax at 2019, Cloud and SAM to match Tmax at 1966, and Evaporation to match Tmax in 1984.  Rain and Cloud are inverted as they have an inverse relationship with temperature.

Figure 1:  10 Year Means of Climate Indicators

Tmax has stayed close to SST until 2001.  Clearly Tmax has increased far more than any of the others, especially since 2001.

The next plots show the difference between decadal averages of Tmax and the other indicators.  Zero difference means an excellent relationship with Tmax.

Figure 2:  Difference: 10 Year Means of Tmax minus Rain and SSTs.

Starting from 1919 (zero difference), Rainfall is close to Tmax until 1957, after which Tmax takes off until it is 1.6 degrees Celsius greater than expected in the 10 years to 2020.  Tmax diverges from SST values in 2001 and in 2020 is 0.7 degrees greater than expected.

In Figure 3, Rain, SST, SAM, and Cloudiness are scaled to match Tmax at 1966.

Figure 3:  Difference: 10 Year Means of Tmax minus Rain, SST, SAM, and Cloud

Figure 3 shows how closely Rain and Cloud are related: differences from Tmax are almost identical.  Compared with 1966, Tmax is 1.3 degrees more than rainfall would suggest in the 10 years to 2020.  SST and the SAM index are less different from Tmax but Tmax divergence is still clear.  You may notice that Tmax differences from all climate indicators seem to change at similar times, apart from SAM in 2015.

In Figure 4, all indicators are scaled to match Tmax at 1984.

Figure 4:  Difference: 10 Year Means of Tmax minus Rain, SST, SAM, Cloud and Evaporation

Differences increase rapidly after 2001, so in Figure 5 indicators match Tmax at 2001.

Figure 5:  Difference: 10 Year Means of Tmax minus Rain, SST, SAM, Cloud and Evaporation

There appears to be a problem with SAM in 2015, and it’s a shame that the BOM have discontinued Cloud and Evaporation observations.  In the last 20 years, it is obvious that Tmax has diverged from other indicators.

Conclusion:

All factors- Rainfall, SAM, SST, Clouds, and Pan Evaporation- point to a clear divergence of temperature nationwide, especially in the last 20 years.  In other words, ACORN-SAT, our official record of temperatures, is unreliable.

How Accurate Is Australia’s Temperature Record? Part 1

January 7, 2021

In my last post I showed that maximum temperature (Tmax) as reported by ACORN-SAT (Australian Climate Observations Reference Network-Surface Air Temperature) appears to be responsible for the growing divergence of the difference between Tmax and tropospheric temperatures from Australia’s rainfall.

In this post I show how Tmax is related to rainfall, and show that while this relationship holds for discrete periods throughout the last 110 years, Tmax has apparently diverged from what we would expect.  In other words, the Acorn Tmax record is faulty and unreliable.

For much of this analysis I am indebted to Dr Bill Johnston who has posted a number of papers at Bomwatch using the relationship between Tmax and rainfall.

At any land based location annual maximum temperature varies with rainfall: wet years are cooler, dry years are warmer.  More rainfall (with accompanying clouds that reflect solar radiation) brings cooler air to the ground; provides more moisture in the air, streams, waterholes, and the soil which cools by evaporation; causes vegetation to grow, the extra vegetation shading the ground and retaining moisture, with transpiration providing further cooling; and in moist conditions deep convective overturning moves vast amounts of water and heat high into the troposphere- especially evident in thunderstorms.  Less rainfall means the opposite: more solar radiation reaches the ground with fewer clouds and less vegetation; there is less moisture available to evaporate; less vegetation growth and transpiration; and much less heat is transferred to the troposphere through convective overturning.

While more rainfall than the landscape can hold results in runoff in rivers and streams, thus removing some moisture from the immediate area, this affects large regions only in tropical coastal catchments- the Kimberleys, the Gulf rivers, the Burdekin and Fitzroy.  Across the bulk of Australia there is very little discharge of water to the oceans.  In the Murray-Darling Basin, on average less than 0.005% of rainfall is discharged from the Murray mouth. (BOM rainfall data and 1891-1985 discharge data from Simpson et al (1993))

This temperature ~ rainfall relationship is particularly evident in desert areas far from any marine influence.  Alice Springs provides a good example.  Figure 1 shows how annual maximum temperatures at Alice Springs Airport vary with rainfall since 1997.  Data are from ACORN.

Fig. 1: Tmax and Rainfall, Alice Springs

The slope of the trend line shows that for every extra millimetre of rain, Tmax falls by 0.0047 of a degree Celsius, which is about half a degree less for every 100 mm.  The R-squared value shows that there is a good fit for the data- 79% of temperature change is due to rainfall.

I said above that this relationship holds for land locations.  An island, with a little land surrounded by water, is mostly affected by sea temperature and wind direction.  Locations near the coast are also affected by marine influence.  At Amberley in south-east Queensland daily maximum temperature can be moderated by the time of arrival of a sea breeze or whether it arrives at all.  (Site changes also can change Tmax recorded.)

Fig. 2: Tmax and Rainfall, Amberley

Further inland, the relationship is strong: at Bathurst, there is 0.4C temperature variation per 100mm of rainfall and 61% of temperature change is due to rainfall alone.

Fig. 3: Tmax and Rainfall, Bathurst

The BOM has sophisticated algorithms for area averaging temperature and rainfall across Australia and provide national climate records back to 1900 for rainfall and 1910 for maxima.  Averaged across Australia individual station idiosyncrasies are submerged so that the 1997 to 2019 relationship between Tmax and rainfall is very strong (and similar to that of Alice Springs):

Fig. 4: Tmax and Rainfall, Australia 1997-2019

However, the relationship is not strong throughout the whole record:

Fig. 5: Tmax and Rainfall, Australia 1910-2019

The relationship from 1910 to 2019 is poor.

In the next figure I compare the Tmax – rainfall relationships for the first 10 years of the record with the last 10 years.

Fig. 6: Tmax and Rainfall, Australia, first and last decades

The trendlines are almost exactly parallel, with tight fits, showing strong relationships 100 years apart- but the trendline for 2010 to 2019 is about 1.7 degrees above that for 1910 – 1919.  How can that be?

It is possible to compare rainfall and temperature throughout the last 110 years.  In the next figure, rainfall is inverted and scaled down so as to match Tmax at 1910.

Fig. 7: Tmax and Inverted Scaled Rain, Australia

Running 10 year means allow us to see long term patterns of rainfall and temperature more easily:

Fig. 8: Tmax and Inverted Scaled Rain, Decadal Means, Australia

Rainfall has increased over the last 110 years (despite what you might hear in the media), and so apparently have maximum temperatures.  In the above figures Tmax and rainfall track roughly together until the mid-1950s, then Tmax takes off.

I calculated an “index” of temperature ~ rainfall variation by subtracting scaled, inverted rainfall from Tmax, commencing at zero in 1919.  This allows us to identify when temperature appears to diverge markedly from inverted rainfall:

Fig. 9: Index of Temperature ~ Rainfall Variation: Tmax minus Inverted Scaled Rain, Decadal Means, Australia

There is a small increase from the mid-1950s, but the really large divergence commences in the 1970s, with the decade from 1973 to 1982 about 0.6 units above the decade to 1972.  The index decreases to 1995, then there is another steep increase to 2007, and a final surge to 2019.

This index alone shows how poorly the official temperature record represents the temperature of the past.

 While there are other times, in the next figures I compare four periods: 1910 to 1972, 1973 to 1995, 1996 to 2007, and 2008 to 2019.  Here I use annual data.

Fig. 10: Tmax and Rainfall, Australia, four periods

Again, trendlines are almost parallel with similar slopes, showing that the Tmax ~ rainfall relationship is fairly constant for all periods- (about 0.5C per 100mm after 1995 and about 0.4C per 100mm before 1995).  However, the lines are separated.  Temperature for each later period is higher than the ones preceding, such that the temperature recorded now is about 1.5 degrees Celsius higher than it would have been for similar rainfall before 1973. And rainfall has increased in that time.

Global Warming Enthusiasts and apologists for the BOM will claim that these breaks between separate periods are real and caused by changes in circulation patterns due to climate change- in particular the Southern Annular Mode.  That will be the subject of Part 2.

Whatever the reasons, Australia wide the Tmax ~ rainfall relationship has remained constant for the past 110 years (as it should) but the temperatures reported in the Acorn dataset have increased by more than 1.5 degrees Celsius relative to rainfall.

Conclusion:

The ACORN-SAT temperature dataset is an unreliable record of Australia’s maximum temperatures.

Surface and Satellite Temperatures: 2020 Update

December 19, 2020

What’s gone wrong?

In November 2015 in my post “Why are Surface and Satellite temperatures Different?” and two follow up posts I showed that the difference is very largely due to rainfall.  You are urged to read these posts in full.

I repeat a key paragraph:

Firstly, surface temperatures are supposed to be different from atmospheric temperatures. Both are useful, both have limitations. The TLT is a metric of the temperature of the bulk of the atmosphere from the surface to several kilometres above the whole continent, in the realm of the greenhouse gases- useful for analysing any greenhouse signals and regional and global climate change. Surface temperature is a metric of temperature 1.5 metres above the ground at 104 ACORN-SAT locations around Australia, area averaged across the continent- useful for describing and predicting weather conditions as they relate to such things as human comfort, crop and stock needs, and bushfire behaviour.

Here are three plots from my 2015 post.

Fig.1:  Tmax and Scaled, Inverted Rain (from Figure 7 from my 2015 post)

Dry periods are hotter, wet periods are cooler.

Fig. 2:  Surface maxima minus atmospheric temperatures and inverted rain (Figure 10 from my 2015 post)

Fig. 3:  Temperature difference compared with rainfall (from Figure 12)

The difference between Australian surface and satellite temperatures was very largely explained by rainfall. However, after five more years of satellite and surface data there is a problem (and I thank Chris Gillham for alerting me to this.)

Fig. 4:  Surface maxima minus atmospheric temperatures and inverted rain

Since about 2013 the difference between surface Tmax and satellite data has visibly increased above rainfall.

Now I have a confession to make.

In previous analyses I used running 12 month means in calculating correlation.  This can lead to inaccuracy as the means can be highly auto-correlated.  From now on I will use annual data, either with calendar years or, as in this post, annual means from December to November (so that summer months and most of the northern Wet season are included in the one datapoint).

I downloaded data from:

Monthly maxima

Monthly rainfall

Temperature of the Lower Troposphere- Australia Land

As with my 2015 post, I have recalculated rainfall and maxima from 1981-2010 means to match UAH.

In the past five years there have been changes:  the Australian maximum temperature record is now based on ACORN-SAT Version 2 instead of Version 1, including new adjustments and some station changes.  No doubt UAH has been tweaked a little as well.

However correlation between the difference between the surface maxima as recorded by Acorn and temperature of the lower troposphere (TLT) as recorded by UAH, and rainfall, has decreased.

Fig. 5:  Temperature difference compared with rainfall

The close connection between the temperature differences and rainfall became broken from about 2005, as can be seen in Figure 4.  Another step up occurred in 2013.

So there appear to be three distinct periods: 1979 to 2004, 2005 to 2012, and after 2013.  Plotting temperature differences against rainfall allows us to compare each period.

 Fig. 6:  Temperature difference compared with rainfall

From 1979 to 2004 and from 2005 to 2012 slopes are identical at 0.4 degrees lower temperature for each 10 mm of rain, with 76% and 93% of temperature variance explained by rainfall. The trend lines are parallel but offset by 0.26 degrees indicating either atmospheric temperatures have reduced or surface maxima have increased in the middle period.  From 2013 the relationship is different with closer to 0.5 degrees lower temperature per 10mm of rainfall, with rainfall explaining 78% of the variance.  Again, the offset shows either UAH has suddenly decreased or Acorn has suddenly increased.

Conclusion:  Something has gone wrong with the relationship between rainfall and temperature in Australia.  In recent years, and certainly since 2013, the surface- atmospheric temperature difference has rapidly increased relative to rainfall.  That should not have happened.

My suspicion is that Acorn’s maxima are to blame.   Figure 1 showed Acorn appeared to step up relative to rainfall in 2001 or 2002, or perhaps earlier in 1997, and again in 2013.  There can be no meteorological explanation for this.

The accuracy, and therefore usefulness, of the ACORN-SAT adjusted temperature record will be the topic of my next post.

Stay tuned.

Acorn Mish-Mash Part 2: Scone

December 13, 2020

In Part 1 we saw that Scone in NSW has the fastest increase in 120 month mean maximum temperatures of all 112 Acorn stations.  The Station Catalogue shows a recent photo of the site with long grass at least 60cm high surrounding the screen- not a very good advertisement for compliance with siting specifications.

Fig. 1:  BOM photograph of Scone site

However the Metadata for this site reveals how much the site has changed.  Before 2005 the screen was close to the runway and a service road, and there was considerable earthworks nearby in 2001.  By April 2005 the screen had been moved to its current location.  In 2012 the grass was 60cm high as in the above photo, and was whipper-snipped during the annual inspection.  In 2015 and 2019 the grass around the instruments was “sparse” as weed control had been used i.e. it had been sprayed out with herbicide.  Temperature data for the airport may be questionable based solely on site information.

The Acorn record has been created by merging data from 01/01/1995 to 31/12/1995 from the present site at the airport with that of a Soil Conservation Research Station (SCS) 10 km away from 1965 to 1994.

Data before 1975 were adjusted downwards because of a change or repair to the screen.

Fig. 2:  Adjustments to annual data at Scone

This resulted in an increase in trend of +0.43C per 100 years.

Fig. 3:  Scone raw and Acorn annual data

However, comparison with the average of the Bureau’s nominated neighbouring stations used to make this adjustment shows the adjustment was much too great.  While the raw record from 1965 to 1973 shows Scone warming 0.29C per 100 years faster than the neighbours, the Acorn record is warming at 1.46C per 100 years- much faster than the neighbours.

Fig. 4:  Difference between Scone and average of neighbours, 1965 – 1973

While that alone is enough to cast doubt on the Acorn adjustments, an analysis of the relationship between maxima and rainfall shows that little reliance can be placed on temperature data before 1974, and after 1995.

At every well maintained site there is a relationship between maximum temperature and rainfall: periods of dry weather are hotter and periods of wet weather are cooler, because of the effects of more or less cloud cover, evaporation and transpiration.  (Wind direction will also have an influence, especially in dry seasons.)  At a well maintained station much more than half of temperature variation is due to rainfall. Therefore, if this relationship varies markedly we can deduce that either temperature or rainfall data are questionable.  This is shown by Dr Bill Johnston at his website, BomWatch, which I urge you to visit, and my analysis is loosely based on his methods.

I calculated 12 month running means of temperature and rainfall for the Airport and the Soil Conservation (SCS) sites.  Figures 5 to 7 show 12 month average temperature plotted against 12 month average rainfall for the three periods, 1965 – 1973 (in which Acorn temperatures are adjusted), 1974 – 1994 (when Acorn and raw are the same), and 1995 – 2018 (when the temperature record switches from the SCS to the airport).

Figure 5:  Scone adjusted maxima plotted against local rainfall

That is a very poor relationship: either temperature data or rainfall data are unreliable.

Figure 6:  Scone unadjusted maxima plotted against local rainfall

Here, more than half of temperature variation can be explained by rainfall.  It is not brilliant, but much better than what comes before and after.

Figure 7:  Scone Airport maxima plotted against local rainfall

While not as bad as pre-1974, less than 30% of temperature variation is explained by rainfall.  Either temperature or rainfall data, or both, are dubious.  Considering the site history and varying vegetation, this is not surprising.

It is unlikely that Acorn is a true record of temperatures at this location. Scone Acorn data are not reliable and should not be included in regional and national climate analysis.

Acorn Mishmash- Part 1: They can’t all be right

November 23, 2020

The Bureau of Meteorology (BOM) produces climate analyses and forecasts based on their best efforts at estimating long term climate trends around the nation- the latest being their suitably scary State of the Climate 2020. 

The main datasets used are ACORN-SAT (Australian Climate Observations Recording Network- Surface Air Temperature) Version 2.1, Daily and Monthly Rainfall Networks, Monthly Pan Evaporation Network, and Monthly Cloud Amount Network.  In future posts I hope to look at some of the BOM’s claims in more detail, however in this series of posts I will look at climate trends at individual stations.  In this post I will look solely at monthly maximum temperatures at all 112 ACORN-SAT sites.  This information is freely available at http://www.bom.gov.au/climate/change/index.shtml#tabs=Tracker&tracker=site-networks and is adjusted and homogenized Acorn V.2.1 data.

Like the Bureau, in order to compare data from all stations I calculate anomalies from monthly means for all months from 1981 to 2010.  I then calculate 120 month running means.  120 month (decadal) means allows us to see long term patterns and changes.  For example, Figure 1 shows decadal monthly means of rainfall that fell at Alice Springs since October 1900. 

Figure 1:  Decadal rainfall at Alice Springs

I would not use the term “cycles” to describe what we see, but clearly there are wetter and drier periods: rainfall is not random from year to year at Alice Springs.

The same decadal averaging when applied to maximum temperatures should show how temperature changes over years, and because Acorn 2.1 is homogenized using neighbouring stations for adjustments, there should be similarities between stations in the same regions.  Let’s see.

I have made all means zero at December 2019 (except Boulia, which ends in June 2013, Point Perpendicular, ending in January 2017,and Gunnedah, ending in June 2019), so in the following plots all data points are relative to the most recent available.  Each data point is the mean of all monthly maxima of the previous 10 years.

Figure 2:  Running 120 month means, maxima anomalies (from 1981-2010 means), relative to most recent data (mostly December 2019), all 112 Acorn stations

That spaghetti plot shows decadal Tmax for all 112 Australian stations, with a few stations identified.  What a mess.  There is a range of 1.5 to 2.5 degrees between highest and lowest in most years before 2000.  We need to look at different regions to make more sense of it.  I will show a map for each region.

Figure 3: Tasmanian stations

Figure 4: Decadal anomalies, Tasmania

Tasmania is a small, compact region, and all stations appear to show the same decadal climate variations.  However, Grove seems to have much less increase than the others, and Larapuna has a much greater increase than its close neighbours, Low Head and Launceston.

Figure 5: East coast of Queensland

Figure 6: Decadal anomalies, east coast of Queensland

Similarity between stations barely extends back as far as 2005.  There is little sign of common climatic patterns except in very recent years.  Brisbane Aero and its closest neighbour Cape Moreton Lighthouse diverge between 1986 and 2007.  And Mackay in particular is an outlier: what reason can there be for Mackay to be more than one degree cooler in all decades to 1940 than Bundaberg to the south and Cairns to the north?

Figure 7:  North-east NSW stations

Figure 8: Decadal anomalies, north-east NSW

Again, while there are some similarities, there is much variety.  Inverell is more than one degree cooler than neighbouring Moree in the decade to the early 1920s, then their decadal means converge to within 0.3 of a degree in the 1950s.  And Scone has had a meteoric rise from 1.6 degrees less than now in November 2001- faster than anywhere else in Australia.

Figure 9:  South-west Western Australian stations

Figure 10: Decadal anomalies, South-west Western Australian stations

This climate region has fairly consistent records, at least back to the 1930s, when Perth’s diverges from the others.  Perth goes from coolest in the 1920s to warmest (relative to now) in the 1980s.

The northern part of Western Australia is messier.

Figure 11:  Northern Western Australian stations

Figure 12: Decadal anomalies, northern Western Australia

Halls Creek and Broome are much cooler than Port Hedland, Marble Bar, and Carnarvon in the decades before the 1930s.  There is a range of 1.3 degrees between decadal means of Marble Bar and neighbouring Karijini North (the former Wittenoom) in 1969, and there is a large divergence between Kalumburu and Carnarvon (at opposite ends of the coast), and the rest of the stations, between 2000 and 2008.

Central Australian stations, because of their remoteness, have a large impact on our climate signal.

Figure 13:  Central Australia

Figure 14: Decadal anomalies, Central Australian stations

While there are similar decadal patterns in maximum temperatures, you will note that Alice’s record rises from the coolest in the 1920s and 1930s to warmest from the 1940s to 1970s, in steps rather than rises and falls.

The Top End is subject to the annual north-west monsoon, with climatic seasons alternating between Wet and Dry.

Figure 15:  Top End stations

Figure 16: Decadal anomalies, Top End

Again we see in most stations rises in the 1970s and 1990s, and falls in the 1980s and early 2000s.  The exception is Darwin, with an almost linear increase with a small acceleration in the 1990s.  Normanton in the far east is an outlier before the 1980s, and VRD in the 1990s.

Inland New South Wales is another region showing common climate patterns, but a few surprises.

Figure 17:  Western NSW stations

Figure 18: Decadal anomalies, western NSW

Here is a good example of many stations showing common climate patterns, rising and falling almost in unison.  However there is still well over one degree between highest and lowest in nearly every year before 1990.  Further, it is not perfect unison: not all stations show similar responses to regional climate swings.  In 1956 and 1957 Canberra at 2.4 degrees cooler than now and Walgett at more than 2.5 degrees cooler are clear outliers, and are well below the pack from 1950 to 1972, and again from 1980 to 2002.  Walgett in particular shows little response to the 1980s surge shown by most other stations.  These two are joined by West Wyalong in the 1970s, and are just under 1.5 degrees cooler than now in 2000 before surging rapidly.

Finally, for comparison, the next plot shows some of the big movers in the Acorn stations, most of which we have seen before.

Figure 19: Decadal anomalies, big hitters

Linearly rising Darwin and recent rapid riser Scone we have met before.  Alice Springs and Perth are joined by Geraldton and Eucla, both in Western Ausralia, in rising from about 2 degrees cooler than now in the decade to the 1920s.  In the 1930s another WA station, Morawa, is almost 2.5 degrees cooler than now.  In the previous figure we saw Canberra in the 1950s 2.4 degrees cooler than now and Walgett more than 2.5 degrees cooler than now: the coolest of any station in Australia. 

Conclusion:

Decadal means show broad patterns of climate change in various regions but there are many examples of individual stations within these regions standing out from these patterns.  They can’t all be right.  The accuracy of the BOM’s ACORN-SAT dataset for maximum temperatures must therefore be called into question at a number of its stations.  This must then throw doubt on the Bureau’s climate analyses and future projections.

In future posts I will look more closely at some of these individual stations’ records.

The Mexican Wave: Covid19 in Australia to October

November 2, 2020

Postscript: For more detailed information and graphs that support/ augment/ supersede my analysis, see https://www.health.gov.au/news/health-alerts/novel-coronavirus-2019-ncov-health-alert/coronavirus-covid-19-current-situation-and-case-numbers

In Queensland we refer to people in the southern states as “Mexicans” (because they’re from “south of the border, down Mexico way” as sung by Gene Autry, Patsy Kline, Patti Page and many others.)

Read on to find why I describe the Australian Covid19 experience from June to October as the Mexican wave.

Worldometers has these plots illustrating the Australian experience:

Figure 1:  Daily new cases

There were (apparently) two waves in Australia.

Figure 2: Cumulative death toll

In four months the death toll increased by 803- more than 770 %! 

We know what went wrong, but the following plots might illustrate it more clearly.

These plots are from statistics from State government websites, such as this one from Victoria: https://www.dhhs.vic.gov.au/victorian-coronavirus-covid-19-data .

All are correct as of 31 October.  They speak for themselves so I will keep my comments to a minimum.

The next figure compares seven day averages of Victorian and all Australian new cases from 25 July to 6 August, at the peak of the “second wave”.

Figure 3: National and Victorian new cases

Until 5 June, Victoria had 1,681 cases.  From then, the new cases began increasing, adding another 18,666 cases to 31 October.  92% of Victorian cases were in this period.

Comparing all states:

Figure 4:  Total cases

Figure 5:  Mortality:

I estimated population figures from March ABS figures.  With almost zero overseas net immigration and very little interstate migration, natural growth remains, which does not change the rates per million by very much at all.

If Victoria was a separate country, its case rate per million would rank it at 127th, just ahead of Bangla Desh.   

Figure 6:  Case Rate per million people

Its Death Rate per million would rank it at 76th, just ahead of Turkey.

Figure 7:  Mortality Rate per million people

The next figure shows Case Fatality Rate, the number of deaths per total cases, which is not complete until the pandemic is over.  These figures are for the CFRs to 31 October.

Figure 8: Covid19 Case Fatality Rate

CFR is affected by whether the virus gets into nursing homes and hospitals which have high proportions of vulnerable people.  There was an outbreak of Covid19 in hospitals in northern Tasmania which affected the Tasmanian CFR.

 4.03% of all Victorian cases so far resulted in death.

The figure for all of Australia is 3.29%.

The figure for Australia excluding Victoria is 1.22%.

The virus first entered Australia via overseas travellers, then spread by local transmission.  The next plot compares infections acquired overseas with those acquired locally in Victoria.

Figure 9: Victorian overseas and locally acquired infections

The contrast is stark.  Victoria compares most unfavourably with other states with over 95% of all cases locally acquired. (Data not available for Tasmania and Territories.)

Figure 10:  Percentage of local transmission in larger states

And Victoria has more than 90% of total national local transmission.

Figure 11:  Percentage of national local transmission

Therefore it can be clearly seen that Australia’s “second wave” was really all about Victoria.  This was easily avoidable with strict hotel quarantine and better contact tracing.  There was no second wave in other states, with small outbreaks mostly due to travellers from Victoria.

Perhaps “Mexican” should from now on describe the government of Victoria, but not their long suffering people, and not governments of NSW, Tasmania, or South Australia.

The Mexican Wave is not something we wish to see repeated.

First Wave Covid19 Mortality in Context

October 22, 2020

Key takeaway points:

  • It is likely that the real Covid19 death toll was at least double the official tally, and possibly hundreds more.
  • Despite this, there were 1,457 fewer deaths in the first six months of this year than last year.
  • The first lockdown worked- until the Victorian fiasco.

In this post I use the most recent Mortality data (released 1 October 2020) from the Australian Bureau of Statistics (ABS), up to 30 June 2020, and the most recent ABS Population data, to examine the effect of the Covid19 pandemic on Australian deaths.  This period covers the whole of the first wave of the pandemic and gives interesting insights.  Future data releases covering the second wave (with another 800 Covid19 deaths) will provide further illumination.

The ABS advises that the data are provisional and not complete as deaths subject to coroners’ inquests are not included, but with completeness percentages in the high 90s “meaningful comparison with historic counts” may be made.

Key statistics from the ABS:

  • 68,986 doctor certified deaths occurred between 1 January 2020 and 30 June 2020.
  • Numbers of deaths have been below historical averages since mid May and below baseline minimums since the week ending 9 June.
  • Deaths from respiratory diseases and heart diseases were below historical minimum counts throughout June.

Figure 1:  ABS chart of deaths and Covid19 infections

The peak of new coronavirus infections was in the week ending 31 March, with 2,428 new infections in that week (Week 13), and the peak in all mortality also occurred in that week.  The following plot shows official Covid19 mortality (from Worldometers) peaking in Week 14.

Figure 2:  Covid19 first wave deaths

The ABS says that the World Health Organisation (WHO) early in 2020 “directed that the new coronavirus strain be recorded as the underlying cause of death, i.e. the disease or condition that initiated the train of morbid events, when it is recorded as having caused death……..

……. Deaths due to COVID-19 are included in the total for all deaths certified by a doctor. They are not included in deaths due to respiratory diseases or any of the other specified causes.”

The first reported Covid19 death was on 1 March, (Week 9).  In Week 14, one week after the peak in new infections, the peak in the first wave deaths occurred.  In this post I define the first wave of the pandemic as Weeks 9 to 21.  (The second wave commenced in Week 24.)  Figure 3 shows Covid19 deaths in context.  The duration of first wave deaths is indicated by the horizontal red line.

Figure 3: Covid19 and total deaths

Note the increase in total deaths in Weeks 12 to 15, and the insignificance of official Covid19 mortality by comparison.  (Australia closed borders on 16 March- Week 11- and began restricting movement in the days following.)

The next graph compares 2020 mortality so far with the five previous years.

Figure 4: Total Australian deaths 2015 – 2020

This year’s peak in deaths also occurred in Weeks 12 to 15, at the height of the first wave infections.

You will also note Australia’s 2020 mortality levelled off well below previous years’ figures, which usually continue rising to peak in Winter and early Spring.  Mortality figures for Weeks 27 to 52 will be very interesting.  There was an unusual early surge in 2019, and a very large increase in deaths in Winter and Spring of 2017.

I now look at excess deaths.  The ABS says:

Measuring ‘excess’ deaths

Excess mortality is an epidemiological concept typically defined as the difference between the observed number of deaths in a specified time period and the expected numbers of deaths in that same time period. Estimates of excess deaths can provide information about the burden of mortality potentially related to the COVID-19 pandemic, including deaths that are directly or indirectly attributed to COVID-19.

… counts of deaths for 2020 are compared to an average number of deaths recorded over the previous 5 years (2015-2019). These average or baseline counts serve as a proxy for the expected number of deaths, so comparisons against baseline counts can provide an indication of excess mortality. “

However, Australia’s population has increased by nearly two million from March quarter 2015 to March quarter 2020 (from 23,745,629 to 25,649,985).  This has a large impact on calculations.  Mortality rate per 1,000 head of population is a better measure. Figure 5 shows mortality rates for recent years.

Figure 5:  Australian mortality rates, 1st 26 weeks, 2015 – 2020

The method I have used is different from the ABS methodology because of the population increase and is based on mortality rates rather than absolute numbers of deaths. 

I have calculated the mortality rate per 1,000 people for each of the 2015-2019 years (using the population for the March quarters of those years), and similarly for the 2020 data.  I then multiply the average of the 2015-2019 mortality rates by the 2020 March quarter population to obtain an estimate of predicted deaths for 2020.  Subtracting this from the actual 2020 number gives an estimate of excess deaths.  An excess death figure of zero indicates the mortality rate is no different from previous years.  The next figure shows plots of actual and expected deaths for the first half of 2020.

Figure 6:  Predicted and actual deaths

Figure 7 is my plot of excess deaths to 30 June.

Figure 7: Estimated Excess Mortality

Excess and actual deaths peaked in Weeks 12 to 15, with weeks 13 and 14 nearly 200 above the expected level- but there were only 56 official Covid19 deaths in those weeks.  Officially, Covid19 was involved in 29 deaths in Week 14, 12 each in Weeks 13 and 15 and only 3 in Week 12.  It is possible that Covid19 deaths were being vastly under-reported in March. 

By the end of June estimated excess deaths were at minus 349, 11.5% below the expected number for Week 26.  Actual deaths in the first half of the year were 1,457 fewer than for the same period in 2019.

States and Territories:

Figure 8 shows actual numbers of deaths for all states and territories.

Figure 8:  2020 mortality numbers for each state

Mortality figures are dominated by New South Wales, followed by Victoria and Queensland.  Figure 9 shows excess deaths.

Figure 9:  Excess mortality by states

Smaller states had smaller changes in excess mortality, although Western Australia had a peak of 54 excess deaths in Week 13.   Figure 10 shows excess deaths for the larger states only.

Figure 10:  Excess deaths in the large eastern states

Peaks in excess deaths occurred between Weeks 9 to 17, but note earlier peaks in New South Wales and Queensland 7 or 8 weeks before the pandemic peak, with Queensland much higher than New South Wales, largely counteracted by Victoria, and a peak in Victoria in Weak 11, counteracted by New South Wales.  There was a third peak in Weeks 17 to 19, coinciding with another peak in Covid19 deaths.  Remember these numbers are additional to Covid19 deaths.  And officially Queensland had only seven Covid19 deaths, almost certainly due to under-reporting.

Age at death

Figure 11 shows the ages at which excess deaths occurred.

Figure 11:  Excess mortality by age

People aged from 0 to 44 years were not affected by the large changes in death rates in older age groups, but there was an increase in excess deaths in the 45 to 64 age bracket in Week 13, at the height of Covid19 infections, as Figure 12 shows.  That looks suspicious, but may be chance.

Figure 12:  Excess deaths for younger cohorts

The majority of excess deaths were in older age groups, as Figure 13 shows.

Figure 13:  Excess deaths for older Australians

There was a peak of 132 excess deaths in those 85 years and over in Week 14, but in Week 13 there were 146 excess deaths in those aged 65 to 84.  There were additional substantial peaks in earlier weeks as well.  It was not a good first half of the year for senior citizens, but excess deaths for all age groups were well below expected numbers by June.

Cause of death

  A death certificate lists all causes of death, and with elderly people these can be three or more.  It is very likely that a person over 85 may die of pneumonia (classified as a respiratory illness), but may also have any or all of dementia, diabetes, cerebrovascular disease, ischaemic heart disease, and cancer.  However, the ABS asks doctors to report the (one) underlying cause of death, and since earlier this year, Covid19 as the underlying cause “when it is recorded as having caused death.

 Figure 14 compares all respiratory deaths with Covid19.

Figure 14:  Covid19 and respiratory deaths

Influenza and pneumonia are subsets of respiratory illness, and the next figure shows interesting excess mortality data for 2020.

Figure15:  Excess deaths due to respiratory causes

Note the peak in respiratory deaths at the height of pandemic infections, but an earlier peak some four weeks previously.  It is likely that Covid19 was not correctly reported to the ABS by all doctors until Week 14 or 15- doctors are human too.  Since the first wave and the increase in personal hygiene, social distancing and little travel, deaths have remained well below previous years.

Figure 16:  Ischaemic heart and cerebrovascular disease excess deaths

This plot illustrates the advances in medicine:  ischaemic heart disease in 2020 had fewer deaths than expected for all of the first six months apart from a peak in Week 7.  Cerebrovascular disease (chiefly strokes) also had fewer deaths than expected except for Week 14 (so was potentially related to Covid19), and another peak in Week 24. 

Figure 17 plots excess deaths caused by the common co-morbidities of Covid19, dementia and diabetes.

Figure 17:  Excess deaths caused by dementia, diabetes, and Covid19

Diabetes and Dementia excess deaths were also higher than expected during the first wave, but there was another large surge in excess deaths with dementia as a cause weeks earlier.

Conclusions:

With the caveat that the ABS mortality figures are provisional, and putting together figures for various states, ages, and causes of death, some conclusions may be drawn:-

Either a mystery respiratory illness or undiagnosed Covid19 was widespread in the eastern states amongst elderly people weeks before the peak of first wave deaths, possibly arriving from cruise ships.

There were probably many more Covid19 deaths and infections than reported.  It is likely that the real Covid19 death toll was at least double the official tally, and possibly hundreds more.

Social distancing, good hygiene, and travel restrictions have caused a large decrease in mortality in May and June by restricting the spread of many common illnesses.  The first lockdown worked- until the Victorian fiasco.

The net effect of the first wave of the Covid19 pandemic on Australian mortality was negative.  Covid19, and public health responses to it, resulted in a lower death toll in the first half of 2020.  This lower death toll was not just in relative (mortality rate) terms but also in absolute terms: there were 1,457 fewer deaths in the first six months of this year than last year.

ABS data for the second half of the year will be released around April 2021 and will provide much better information about excess mortality for all states (and Victoria in particular), for all age groups, and for all causes.

I include an appendix with raw mortality data for 2015 -2020.

Appendix:  Raw mortality data for all causes 2015 – 2020.

Figure 18:  Respiratory mortality

Note the typical winter and spring surge in respiratory deaths, mainly due to influenza outbreaks in cold months.  There was an early surge in 2019 and a very large surge in 2017 which will skew means for those weeks.  Median mortality rate may be more appropriate than means.

Figure 19:  Ischaemic heart disease mortality

Heart disease mortality has been below previous years for most of the first 26 weeks.

Figure 20:  Cerebrovascular disease mortality

Cerebrovascular disease (stroke) deaths peaked during the first wave of Covid19 but have been mostly near the bottom of the range of previous years, with a second peak in June.

Figure 21:  Dementia mortality

Deaths with dementia as a cause have increased over the years.  A peak in dementia deaths coincided with Covid19 but deaths have been in the normal range since then.

Figure 22:  Diabetes mortality

A peak in diabetes deaths coincided with the peak in Covid19 infections and deaths, and was much higher than expected.  At the end of June deaths were in the range of previous years.

Figure 23:  Cancer mortality

Cancer deaths have increased over the years and 2020 remains within the expected range.  You may note there is no winter increase in cancer mortality.

Distance Records for Temperature Adjustments

October 6, 2020

Trigger Warning:  ridicule of the Australian Bureau of Meteorology below!

The official Australian climate record is developed from ACORN-SAT– the Australian Climate Observation Reporting Network- Surface Air Temperatures.  This is relied on by governments and industry and so should be completely trustworthy and free from any problems that might lead to lack of confidence.

The Acorn stations have had their temperature records adjusted to account for any discontinuities or irregularities.  This is done by comparing Acorn stations’ data with those from a selection of comparative stations. 

The Bureau says:

The process of homogenisation seeks to answer a very simple question: what would Australia’s long-term temperature trend look like if all observations were recorded at the current sites with the current available technology? Homogenisation means we can compare apples with apples when it comes to temperature trends.

One might expect that, with the aim being to “compare apples with apples”, stations used for comparison and making adjustments would be physically not too distant- ideally, neighbouring.  

Not so.

For many stations, not even remotely so.

Australia is a very big country with vast areas of sparsely inhabited desert.  There are very large distances between towns in the outback, so it is not surprising that it is often difficult to find suitable comparative stations.

But the Acorn Station Catalogue, which has helpful lists of comparative stations used for adjustments, has some absolute doozies.  Here are some for your amusement.  (Obviously most stations have many comparative sites.)

Carnarvon, in Western Australia, has been adjusted by reference to a number of stations hundreds of kilometers away, including Southern Cross, only 897km away.

Camooweal, Queensland, “ “ “  Thargomindah, 1,067km away.

Boulia, Qld, “ “ “  Walgett, New South Wales, 1,130km

Halls Creek, WA, “ “ “  Boulia, Qld, 1,370km

Tennant Creek, Northern Territory, “ “ “   Charleville, Qld,  1,443km

Mount Gambier, in South Australia, has been adjusted with the help of Lismore in northern New South Wales, 1,526km away.  (And it’s not as if there is a shortage of sites in this well populated part of South Australia.)

But the gong, the gold medal, the record breaking achievement for the Bureau, goes to…….

Alice Springs, in the Northern Territory, which has been adjusted using data from Collarenebri in New South Wales,  1,590 kilometres away.

And they want the public to trust them.

More Questionable Adjustments- Cape Moreton

October 5, 2020

Here’s another Acorn station with interesting adjustments- Cape Moreton (40043) minimum temperatures.

Cape Moreton Lighthouse is on the north-eastern tip of Moreton Island, 65 km north-east of Brisbane.  It is not compliant with siting specifications. 

Figure 1 is the adjustment summary shown by the Bureau in its Station Catalogue.

Figure 1:  Adjustment summary for Cape Moreton

Two points to note:  The Bureau has TWO adjustments applied to the same date- 1/01/1946; and there are four comparative stations used to make these adjustments at this Acorn station.

Figure 2 shows the neighbours the BOM used for comparison. 

Figure 2:  Google Maps image showing Cape Moreton and its neighbours

There are many neighbouring stations the Bureau could have used for comparison, but the Bureau chose those with the “best correlation” during the comparison period (the late 1940s):  Brisbane Regional Office 65km away, and probably affected by Urban Heat Island effect; Yamba, also coastal but 267km south; Dalby Post Office on the Darling Downs 220km west; and Miles Post Office 330km west.

Figure 3 shows the annual average minima for these weather stations.

Figure 3:  Annual minima, Cape Moreton and neighbours

UHI at Brisbane is visible as the plot line rises faster than the others after 1950.

The next figure shows Acorn’s adjustments have increased the rate of warming from +1.2 degrees Celsius per 100 years to more than +1.5 C.

Figure 4:  Cape Moreton Minima

Figure 5 shows the difference between the original raw record and Acorn.

Figure 5:  Cape Moreton adjustments

It is plainly obvious that the Acorn adjustment summary shown in Figure 1 is wrong.  The first adjustment was applied from 01/01/1948 (not 1946) and decreased the annual minima for 1946 and 1947 by -1.2C.  The second adjustment was applied from 01/01/1946 and increased previous annual minima by +0.8C or +0.9C. The raw minima were decreased by -0.3C or -0.4C, but that is not how the Bureau describes the adjustment process:

Date applied: data prior to this date was adjusted for the reason (cause) cited. Adjustments are superimposed on each other; for example, if two adjustments are shown, one for 1/1/2000 and one for 1/1/1988, data prior to 1/1/1988 has both adjustments applied to it, data between 1/1/1988 and 1/1/2000 only has the first adjustment applied, and data after 1/1/2000 is not adjusted at all.”

The documentation of Acorn is a mess.

In order to compare data from stations with varying temperatures we need to calculate their anomalies from their means for the same period.  Figure 6 shows Cape Moreton’s and comparison stations’ anomalies from their 1931-1960 means.

Figure 6:  Minima anomalies, Cape Moreton and “neighbours”

Hard to follow, there is too much variability.  You may note that by comparison with the periods before 1948 and after 1960, the 1950s show much agreement.  The next figure shows the period from 1930 to 1960.

Figure 7:  Minima anomalies, Cape Moreton and “neighbours” 1930-1960

Notice that in 1946 and 1947 (indicated by the arrow) Cape Moreton is far too warm- the reason for the adjustment; however Yamba’s record is just as erratic or more so, being too low in 1933, 1934, 1940-1944,  and 1947; and too high in 1950.  This suggests firstly that the 1946 and 1947 adjustments were justifiable for those two years, and secondly that Yamba is not a good comparison station.  The next figure, with Yamba excluded, clearly illustrates this point.

Figure 8:  Cape Moreton and comparison stations, excluding Yamba

Apart from 1946 and 1947 Cape Moreton’s record is not greatly dissimilar from the three remaining stations. 

The object of adjusting temperatures using neighbours for comparison is to endeavour to produce a record that more truly reflects climate trends of the area.  The resulting record should be more like the neighbours than the original raw record.  We can test this by plotting the differences between Acorn and the raw record and the average of the neighbours.  If the comparison is good, while individual years’ differences may vary, the trend should be close to zero: the station should not be warming or cooling more than the neighbours.  Figure 9 shows the results for Cape Moreton minima for the period before the 1946 adjustment, excluding Yamba from the average.

Figure 9:  Differences between Cape Moreton and Qld neighbours

You will note that the blue trend line, showing the trend of the difference between Cape Moreton’s annual data and the average of Brisbane, Dalby, and Miles, has a trend of about +0.3 degrees per 100 years, indicating Cape Moreton is already warming more than the others.  The “raw” record already compares fairly well with the neighbours, considering that they are inland stations, unlike Cape Moreton.  In contrast the red trend line shows the adjusted data is warming more than three times faster, indicating a poor reflection of the climate of the area.

Conclusion

The Bureau has not followed its own methodology in its adjustment summary.

Documentation of adjustments is incorrect.

Three comparison stations are hundreds of kilometres away and another is subject to Urban Heat Island effect.

One comparison station (Yamba) has a record more erratic than Cape Moreton’s and should not have been used.

The adjustments have increased the difference between Cape Moreton and its neighbours, and has increased the warming trend by 30%.

Garbage in, garbage out.

Sources for annual minima data:

Acorn: http://www.bom.gov.au/climate/change/hqsites/data/temp/minT.040043.annual.txt

Raw:

Cape Moreton:

http://www.bom.gov.au/jsp/ncc/cdio/weatherData/av?p_nccObsCode=38&p_display_type=dataFile&p_startYear=&p_c=&p_stn_num=40043

Brisbane Regional Office: http://www.bom.gov.au/jsp/ncc/cdio/weatherData/av?p_nccObsCode=38&p_display_type=dataFile&p_startYear=&p_c=&p_stn_num=40214

Dalby Post Office:

http://www.bom.gov.au/jsp/ncc/cdio/weatherData/av?p_nccObsCode=38&p_display_type=dataFile&p_startYear=&p_c=&p_stn_num=41023

Miles Post Office:

http://www.bom.gov.au/jsp/ncc/cdio/weatherData/av?p_nccObsCode=38&p_display_type=dataFile&p_startYear=&p_c=&p_stn_num=42023

Yamba Pilot Station:

http://www.bom.gov.au/jsp/ncc/cdio/weatherData/av?p_nccObsCode=38&p_display_type=dataFile&p_startYear=&p_c=&p_stn_num=58012

Garbage In, Garbage Out- Horn Island

September 27, 2020

After last year’s major project of checking the compliance of 666 Australian weather stations with the guidelines set out by the Bureau of Meteorology, and finding that nearly half did not meet siting specifications, I decided to take a break from analysing BOM climate data.

It might now be time to re-enter the fray.

The pride of the BOM climate fleet, Version 2 of the Australian Climate Observation Recording Network- Surface Air Temperatures (ACORN-SAT, or Acorn 2) was launched with no fanfare at all in late 2018 and now is the basis for the Bureau’s climate claims and predictions.  I summarised many of its faults in May 2019.

Now I am going to look at some individual examples of Acorn 2 nonsense- firstly, Horn Island’s minimum temperatures.

Horn Island is our most northerly Acorn site.  It is the airport for Thursday Island and is closer to Port Moresby than any other Australian town except Weipa and of course TI.  Figure 1 shows the station’s only neighbours on Cape York Peninsula the BOM used for comparison. 

Figure 1:  Google Maps image of Cape York Peninsula showing Horn Island and its neighbours

Figure 2 shows the annual average minima for all of these weather stations.

Figure 2:  Annual minima, Horn Island and neighbours

Horn Island 27058 has a very limited data record commencing in March 1995.  To construct a longer record back as far as 1951, BOM merged Horn Island’s observations with those of Thursday Island Township 27021 and Thursday Island Meteorological Office 27022.  Figure 3 shows the result of the BOM’s merge.

Figure 3:  Horn Island and Thursday Island minima

The 27022 TI Met Office annual average Tmin has been reduced in Acorn by 0.5 or 0.6 degrees for all years before 1993.  (The Adjustment Summary claims there was an annual impact of -0.53 degrees.)  There were other adjustments at 01/01/1967 and 01/01/1958, but the 1993 adjustment has the largest effect.

Unfortunately, there are only 6 months of overlap between 27021 and 27022 (September 1992 to February 1993), and 11 months between 27021 and 27058 (March 1995 to January 1996), as figure 4 shows.

Figure 4:  Extent of overlaps in monthly minima

In September, October, and November 1992 TI Township 27021 recorded minima 1.1 to 1.4 degrees cooler than the Met Office site in “a relatively exposed location” on top of the hill 900 metres west.  However, Acorn tracks 27021 and approximately splits the difference from 27022.

Figure 5:  Extent of overlaps in monthly minima, including Acorn 2

To do this, the BOM uses two comparative stations, Lockhart River 28008 and Coen Airport 27006.  Coen is 385km from Horn Island. 

Figure 6 shows a plot of monthly anomalies from 1981 to 2010 means for these stations.

Figure 6:  Monthly minima anomalies from 1981-2010 means, all stations

Note that 27022 is close to 27021 from December 1992 to February 1993, but not before, while Coen is nothing like either.  I cannot see the justification for the adjustment.

I constructed a merge of annual data using 27021 for 1993, 1994, and 1995, joined to 27022 and 27058 with no adjustment to construct a “raw” record.  This is entirely artificial, but no more so than Acorn.  Figure 7 shows plots of both with annual trends.

Figure 7:  Horn Island minima, Acorn 2 and “raw”

Note Acorn has enormously increased the warming trend.  Figure 8 plots the differences between Acorn and my “raw” record.

Figure 8:  Horn Island minima adjustments

The earlier adjustments were also large and based on “statistical” breakpoints.

The object of adjusting temperatures using neighbours for comparison is to endeavour to produce a record that more truly reflects climate trends of the area.  The resulting record should be more like the neighbours than the original raw record.  We can test this by plotting the differences between Acorn and the raw record and the average of the neighbours.  If the comparison is good, while individual years’ differences may vary, the trend should be close to zero: the station should not be warming or cooling more than the neighbours.  Figure 9 shows the results for Horn Island minima for the period that the 1993 adjustment applied.

Figure 9:  Differences between Horn Island and two neighbours

You will note that the blue trend line, showing the trend of the difference between annual data merged with no adjustment and the average of Coen and Lockhart River, is almost flat, indicating the “raw” record already compares well with the neighbours.  In contrast the red trend line shows the adjusted data is warming faster than the neighbours, indicating a very poor reflection of the climate of the area.

Conclusion

The Horn Island record should never have been merged because of the lack of suitable overlap.

Once merged, it should never have been adjusted downwards so much.

 Lockhart River and Coen are far too distant to be suitable for comparison.

The result is nonsense.

Garbage in, garbage out.

CO2vid Watch: August

September 10, 2020

I have been wondering 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.

Earlier 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.”

The CO2 concentration number for August is now published: 412.55 p.p.m. (parts per million).  The seasonal drawdown of CO2 has begun, but CO2 concentration is still 2.61 ppm above the figure for August last year.  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.

Fig. 1:  12 month change in CO2 concentration since 2015 to August 2020- Mauna Loa

Figure 2 is a monthly update for 2020 I will show as each month’s CO2 figures become available (and 2021 if necessary):

Fig. 2:  Updated 12 month changes in CO2 concentration for 2020- Mauna Loa

Figure 3 shows the 12 month change in CO2 concentration since the record began.

Fig. 3:  12 month change in CO2 concentration since 1958 to August 2020- Mauna Loa

Annual growth has been above zero since the mid 1970s, and has not been below 1 ppm since 2011. The annual rate of change is increasing, in other words CO2 concentration growth is accelerating.

Note that so far this year, 12 month changes continue to remain firmly in the normal or even upper range, and there is no sign of any slow down. And there won’t be!

This paper by J. Reid explains why.

http://blackjay.net/?p=1021%20%3Chttp://blackjay.net/?p=1021%3E

CO2 growth appears to be an entirely natural process.

Unless something dramatic happens, I don’t think I will continue this series any longer. There’s nothing to see.

An Impossibility of Windmills

September 9, 2020

There are many strange collective nouns for groups of animals, people, and things. For example, a parliament of owls, a murder of crows, a convocation of eagles, an intrusion of cockroaches, an audience of squid are for groups from the animal kingdom.

A company of archers, an eloquence of lawyers, and a poverty of pipers describe some groups of people.

What about things? A distraction of smartphones, a smug of Priuses, a Hilary of pantsuits I have heard of.

But Jan Smelik from the Netherlands has sent me a link to his Youtube video and we now have collective noun for a group of windmills.

No, not the old windmills for pumping water and grinding grain we know from paintings and tourist brochures- the modern variety which will save the world from global warming.

Very appropriately, an impossibility of windmills.

Here’s his video:

Even more so for Australia!

CO2vid Watch: July

August 7, 2020

I have been wondering 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.

Earlier 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.”

The CO2 concentration number for July is now published: 414.38 p.p.m. (parts per million).  The seasonal drawdown of CO2 has begun, but CO2 concentration is 2.61 ppm above the figure for July last year.  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.

Fig. 1:  12 month change in CO2 concentration since 2015 to July 2020- Mauna Loa

Notice the amount of 12 month change has increased a bit more.

Figure 2 is a monthly update for 2020 I will show as each month’s CO2 figures become available (and 2021 if necessary):

Fig. 2:  Updated 12 month changes in CO2 concentration for 2020- Mauna Loa

Note that so far this year, 12 month changes continue to remain firmly in the normal or even upper range, and there is no sign of any slow down.

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

CO2vid Watch: June

July 13, 2020

I have been wondering 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.

Earlier 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.”

(In a coming post I will update my expectations for the end of the year and next year.) 

The CO2 concentration number for June is now published: 416.39 p.p.m. (parts per million).  The seasonal drawdown of CO2 has begun, but CO2 concentration is 2.47 ppm above the figure for June last year.  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.

Fig. 1:  12 month change in CO2 concentration since 2015 to June 2020- Mauna Loa

Notice the amount of 12 month change has increased a little.

Figure 2 is a monthly update for 2020 I will show as each month’s CO2 figures become available (and 2021 if necessary):

Fig. 2:  Updated 12 month changes in CO2 concentration for 2020- Mauna Loa

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

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

Hottest Day Ever in Australia Confirmed: Bourke 51.7°C, 3rd January 1909

July 11, 2020

reposted from Jennifer Marohasy

The Australian Bureau of Meteorology deleted what was long regarded as the hottest day ever recorded in Australia – Bourke’s 125°F (51.7°C) on the 3rd January 1909. This record* was deleted, falsely claiming that this was likely some sort of ‘observational error’, as no other official weather stations recorded high temperatures on that day.

However, Craig Kelly MP has visited the Australian National Archive at Chester Hill in western Sydney to view very old meteorological observation books. It has taken Mr Kelly MP some months to track down this historical evidence. Through access to the archived book for the weather station at Brewarrina, which is the nearest official weather station to Bourke, it can now be confirmed that a temperature of 50.6°C (123°F) was recorded at Brewarrina for Sunday 3rd January 1909. This totally contradicts claims from the Australian Bureau of Meteorology that only Bourke recorded an extraordinarily hot temperature on that day.

Brewarrina Meteorological Observations Book, January 1909 — photographed by Craig Kelly MP. Note 123F recorded at 9am on 4th January 1909.

Just today, Friday 10th July 2020, Mr Kelly MP obtained access to this record for Brewarrina, the closest official weather station to the official weather station at Bourke.

He has photographed the relevant page from the observations book, and it shows 123°F was recorded at 9am on the morning of Monday 4th January 1909 – published here for the first time. This was the highest temperature in the previous 24 hours and corroborates what must now be recognised as the hottest day ever recorded in Australia of 51.7°C (125°F) degrees at Bourke on the afternoon of Sunday 3rd January 1909.

The Meteorological Observations Book for Bourke for January 1909 records 125°C for 3rd January. Photograph taken on 26th June in 2014 at the Chester Hill archive by Jennifer Marohasy.

That the Bureau of Meteorology denies these record hot days is a travesty. Is it because these records contradict their belief in catastrophic human-caused global warming?

The temperature of 50.6°C (123°F) recorded back in 1909 which is more than 100 years ago, photographed by Mr Kelly today at the National Archives in Chester Hill, is almost equivalent to the current official hottest day ever for Australia of 50.7 degrees Celsius at Oodnadatta on 2nd January 1960. These are in fact only the fourth and third hottest days recorded in Australia, respectively.

Not only has Mr Kelly MP tracked-down the meteorological observations book for Brewarrina, but over the last week he has also uncovered that 51.1°C (124°F) was recorded at White Cliffs for Wednesday 11th January 1939. This is the second hottest ever!

The evidence, a photograph from the relevant page of the White Cliff’s meteorological observations book, is published here for the first time.

This photograph from the White Cliffs Meteorological Observation Book shows the second hottest temperature ever recorded in Australia using standard equipment in a Stevenson screen.

Until the efforts of Mr Kelly MP, this second hottest-ever record was hidden in undigitised archives.

It is only through the persistence of Mr Kelly to know the temperatures at all the official weather stations in the vicinity of Bourke that this and other hot days have been discovered.

If we are to be honest to our history, then the record hot day at Bourke of 51.7°C (125°F) must be re-instated, and further the very hot 50.6°C (123°F) recorded for Brewarrina on the same day must be entered into the official databases.

Also, the temperature of 51.1°C (124°F) recorded at White Cliffs on 12th January 1939 must be recognised as the second hottest ever.

For these temperatures to be denied by the Bureau because they occurred in the past, before catastrophic human-caused global warming is thought to have come into effect, is absurd.

At a time in world history when Australians are raising concerns about the Chinese communist party removing books from Libraries in Hong Kong, we should be equally concerned with the Australian Bureau of Meteorology removing temperature records from our history.

If global warming is indeed the greatest moral issue of our time, then every Australian regardless of their politics and their opinion on greenhouse gases and renewable energies, must be honest to history and these truths.

____

* This temperature (125°F/51.7°C on the 3rd January 1909) was recorded at an official Bureau weather station and using a mercury thermometer in a Stevenson screen. Hotter temperatures were recorded in 1896 but the mercury thermometers were not in Stevenson screens, which is considered the standard for housing recording equipment.

The feature image shows Craig Kelly MP at The Australian National Archive, Chester Hill, just today examining the Brewarrina Meteorological Observations book.

The following YouTube video is of me being interviewed on Sky Television by Chris Smith last December 2019.

I have previously blogged on the record hot day at Bourke being deleted by the Bureau here:
https://jennifermarohasy.com/2017/02/australias-hottest-day-record-ever-deleted/

Covid-19 and Global Warming: Two Problems, Two Responses

June 24, 2020

Skeptics have often faced the argument, “You trust medical experts, so you should trust the climate experts”.  The science, after all, is settled.

That argument is nonsense- there is no comparison between them.

Medical researchers, in the fight against Covid-19, are using the time honoured scientific method used for decades in the search for treatments, vaccines, or cures for a host of crippling and deadly diseases- cancer, diabetes,  HIV, to name a few.

This usually involves years of careful examination of patient data and all existing information and literature, forming an hypothesis to test, designing studies, writing protocols, implementing and evaluating laboratory trials, designing and conducting animal trials, designing and conducting clinical trials, analyzing results, and then reporting findings.  It is a continuous process built on past and current evidence. 

The sought-after treatment or vaccine must pass the tests of safety and efficacy.  Doctors are enjoined: First, do no harm.  As well, the treatment must be effective.  There are many examples of trials that were stopped because they were causing higher risk of harm or were showing no benefit. 

It would be too much to expect automatic success from any of the programs under way around the world to find a safe and effective Covid-19 vaccine.

The same approach is not used in climate science:-

It is assumed that the patient (the world) has an unusually high and increasing temperature, even though patient records indicate periods of higher temperature in the past.

It is assumed that this will continue and will worsen.

It is assumed that this is dangerous and must be treated.

It is assumed that we know the cause, because of an untested hypothesis that increasing concentrations of greenhouse gases in the atmosphere, caused by the burning of fossil fuels, lead to increasing temperatures.

It is assumed that “the science is settled”, (and, even more dangerously, conflicting opinions have been actively suppressed.)

Based on these assumptions, all manner of treatments have been rushed into service, with no testing and no thought for safety or efficacy.   Unwanted and dangerous side-effects have been ignored.  Enormously expensive treatments with no proven or even possible benefit have been implemented, while other treatments (e.g. nuclear energy) are beyond consideration.

Why do I trust medical experts?

When discussing a cancer diagnosis, I trusted my specialist because he showed me the evidence, welcomed a second opinion, discussed the benefits and side-effects of different treatments (and none), gave me research papers on the safety and efficacy of the recommended treatment, and gave me time to think about it.  Nearly three years later the treatment is (so far) successful.

Thank God climate experts are not involved in the search for a Covid-19 vaccine- or cancer treatment.

A Closer Look at CO2 Growth

June 11, 2020

For a while I have been looking at atmospheric carbon dioxide data from stations around the world.  This post draws together some observations, many of which are pretty much common knowledge- but some of what I’ve found is surprising.

So I’ll start by listing some of this common and not so common knowledge:-

-The often quoted figures for global CO2 levels are not at all global, but are the local readings at Mauna Loa in Hawaii.

-The long term carbon dioxide record shows continuing increase at all stations, indicating greater output than sinks can absorb. 

-Southern Hemisphere CO2 concentration is increasing but more slowly than the Northern Hemisphere.  Their trends are diverging.

-Seasonal peaks in CO2 concentration occur in late winter and spring in both hemispheres.

-There is very great inter-annual variation in the seasonal cycle of CO2, which can be even more than the average annual increase.

-This inter-annual variation occurs at the same time in both hemispheres, even though the seasonal cycles are 6 months apart.  This implies a global cause, such as the El Nino Southern Oscillation (ENSO).  Large volcanic eruptions also have an impact.  There are likely to be other factors.

-Sea surface temperature change precedes CO2 change by 12 to 24 months.  It is difficult to reconcile this with ocean out-gassing as a cause of the inter-annual CO2 changes.  It is nonsense to claim that CO2 change leads to sea surface temperature change.

-ENSO changes occur at about the same time as CO2 changes.

-CO2 concentration increases during La Ninas. 

-El Ninos precede higher sea temperatures by 4 to 6 months.

-Because of the “oscillation” part of ENSO events, strong events are followed by opposite conditions 16 to 24 months later.  In this way a strong El Nino will lead to strong ocean warming often followed by La Nina conditions and higher CO2 concentration.

-The slowing Southern Hemisphere trend and flattening curve at the South Pole lacks satisfactory explanation.

CO2 measuring stations

Geoffrey Sherrington has shown differences existing between NOAA and Scripps daily CO2 data at Mauna Loa, and that uncertainty in daily data must be much greater than the claimed 0.2 part per million.  His article confirmed my decision to use Scripps instead of NOAA data.  In this post I use Scripps monthly data from many stations across the Pacific, and data from the CSIRO station at Cape Grim in Tasmania, to compare observations from different locations.

Figure 1 shows the locations of stations in the Scripps network, and Cape Grim.

Figure 1:  Scripps stations and Cape Grim

Point Barrow is the most northerly part of the USA, and Alert is the most northerly part of Canada.

The often quoted figures for global CO2 levels are not at all global.  They are not the global average, nor are they representative of other locations.  They are in fact the local CO2 concentration from the slopes of Mauna Loa in Hawaii.  The trend in CO2 increase is similar to, but not the same as, those in other locations.

Figure 2 shows monthly CO2 concentrations from all of the Scripps stations.

Figure 2:  Monthly CO2 at all locations

It is clear that all stations show a similar rising trend, and all show seasonal variation of varying degrees.  However, few stations have long term records, and most have periods of missing data. 

Differences, similarities, and divergence

Figure 3 shows monthly differences from the Mauna Loa record of stations with fairly complete records. 

Figure 3:  Six stations’ difference from Mauna Loa

Monthly differences show huge seasonal variation, so Figure 4 shows 12 month average differences.

 Figure 4:  Six stations’ difference from Mauna Loa, 12 month averages

Clearly, there are major differences between the different records: 

-La Jolla has too many gaps for further analysis. 

-There are differences between Cape Grim and South Pole from about 1980 to the early 1990s.

-Southern Hemisphere stations (American Samoa, Cape Grim, and South Pole) are diverging from Mauna Loa, and from Barrow Point and Alert.  Figure 5 shows these trends more clearly.

Figure 5:  Barrow Point and South Pole difference from Mauna Loa, 12 month averages

While South Pole and Mauna Loa are strongly diverging, Barrow Point and Mauna Loa are becoming slightly more similar.

In Figure 6, the divergence of South Pole data is evident in monthly readings.

Figure 6:  Monthly CO2 concentrations, Mauna Loa, Barrow Point, and South Pole

Note how much larger the Barrow Point seasonal range is.  More importantly, note how South Pole data begin well within the Mauna Loa range, but 50 years later barely reach the bottom of the Mauna Loa range, as Figures 7 and 8 show.

Figure 7:  Monthly CO2 concentrations, Mauna Loa and South Pole 1965-1975

Figure 8:  Monthly CO2 concentrations, Mauna Loa and South Pole 2010 -2020

Why the divergence?  How can a well-mixed gas show a lower trend at the South Pole?  Why is it that the South Pole summer draw down has decreased and is now a plateauing?

Seasonal change

Now zooming in to look at seasonal swings in just two years, 2011 and 2012:

Figure 9:  Monthly CO2 concentrations, Mauna Loa, Barrow Point and South Pole

The Barrow Point range from low to high is nearly three times the size of the Mauna Loa range, and the South Pole range is tiny.  The peak concentrations at Barrow Point and Mauna Loa are in late spring, with a sharp drop at Barrow Point to August and a smoother curve at Mauna Loa to lows in autumn; while at the South Pole the annual curve is better described as a shallow rise in winter followed by a “peak” in spring and a long plateau over summer, with a very small decrease in late summer.  The next three plots show the timing of highs and lows at these three stations for the whole record.

Figure 10:  Timing of seasonal high and low CO2 concentrations, Mauna Loa

Annual lows are in September or October, and highs are almost always in May.

Figure 11:  Timing of seasonal high and low CO2 concentrations, Barrow Point

Lows are always in August, while highs are spread across late winter to late spring, with a plateau from February to May (and extending twice into June).

Figure 12:  Timing of seasonal high and low CO2 concentrations, South Pole

At the South Pole, seasonal highs are reached in spring or early summer, with lows in late summer and early autumn, with one instance in June.

Inter-annual changes

While the seasonal cycles appear to be regular, the timing and size of seasonal changes can vary considerably from year to year.

The next plots show detrended data since 1985 for several locations (few have good data before 1985).  Detrending allows us to compare inter-annual variation more easily.  We do this for each record by subtracting the trend.

Figure 13:  Detrended monthly CO2, Mauna Loa

Figure 14:  Detrended monthly CO2, Barrow Point and Alert

Figure 15:  Detrended monthly CO2, South Pole and Cape Grim

While the seasonal range is different for each location, there is remarkable similarity in timing of changes, for example the late 1980s- early 1990s and 2009-2013.  Note how close Cape Grim and South Pole are, although Cape Grim is at 40.68 degrees South, 49 degrees north of the South Pole.  The South Pole data appear to be representative of a large part of the Southern Ocean.

Because the detrended data retain enormous seasonal variations, it is necessary to show the detrended data (this time from 1979) with monthly means subtracted, for Barrow Point in the far north, Mauna Loa in the middle, and South Pole at the extreme south.  Here are the seasonal signals:

Figure 16: Seasonal signals of monthly CO2 data

As an example, Figure 17 compares detrended data from Barrow Point with monthly means:

Figure 17:  Detrended monthly CO2 with monthly means, Barrow Point

Subtracting the monthly means shows the residual variation in carbon dioxide for Barrow Point:

Figure 18:  Detrended monthly CO2 with seasonal signal removed, Barrow Point

Figure 19 combines the three stations:

All three records follow the same pattern, with a large increase from 1979 to the late 1980s, followed by decrease in the 1990s.  There appears to be another steep increase from 2012 to the present.  Notice that Mauna Loa and South Pole values can be from 1 ppm below to 2 ppm above the trend, while at Barrow Point the range can be from 4ppm below to 5 ppm above the trend, which is about 2.5 ppm per year. 

However, there is still a large amount of variation in the monthly figures.  A centred 13 month rolling mean makes comparison much easier.

Figure 20:  Centred 13 month mean of detrended monthly CO2 with seasonal signal removed

The similar pattern followed by stations from north to south, from the Arctic Ocean, across the Pacific, to the Antarctic, far from any industrial or cropping contamination, is immediately obvious.  The Barrow Point record appears to lag behind Mauna Loa and South Pole data by from one to five months.  South Pole can be a few months ahead to a few months behind Mauna Loa, even though South Pole absolute monthly concentration peaks are from four to seven months later.

Ocean temperature effects

In Figure 14 of my post on 2nd May, Will Covid-19 Affect Carbon Dioxide Levels? I showed that CO2 change lags one year behind sea surface temperatures (SSTs).  The next plot shows the centred 13 month mean of HadSST4 data, scaled up to compare with CO2 data.

Figure 21:  Scaled, centred 13 month mean of detrended monthly HadSST4 and CO2 data with seasonal signal removed

Now the same data with SSTs lagged 12 months…

Figure 22:  Scaled, centred 13 month mean of detrended monthly HadSST4 and CO2 data with seasonal signal removed, HadSST4 lagged 12 months

Large change in CO2 concentrations appears closely linked with sea surface temperature a year before- (or even two years, as between 2002 and 2010).  Sea surface temperatures have a global effect.

ENSO effects

Another cause of CO2 variation is the El Nino- Southern Oscillation (ENSO) which appears in the swings between El Nino and La Nina conditions.  ENSO has a great effect on weather conditions globally, affecting winds, clouds, rainfall and temperature.  Figure 18 shows how CO2 levels respond to the Southern Oscillation Index (SOI), which is a good indicator of ENSO conditions.

Figure 23:  Centred 13 month means, scaled SOI and detrended CO2 levels

CO2 increases in La Ninas.  The pattern becomes more intriguing when we plot inverted SOI levels with sea surface temperatures, as in Figure 19.

Figure 24:  Scaled, centred 13 month mean of detrended monthly HadSST4 with seasonal signal removed and scaled inverted SOI

Inverted SOI data indicate SST data 4 to 6 months later.  (The early 1980s and early 1990s don’t match because of the huge volcanic eruptions of El Chichon and Pinatubo.)  In other words, an El Nino will raise ocean temperatures, and a La Nina will lower ocean temperatures, 6 months later.  Because of the oscillating nature of ENSO, El Ninos and La Ninas approximately reflect each other 16 to 24 months later, as Figure 20 shows.  (Again, El Chichon and Pinatubo have a large impact.)

Figure 25:  Scaled SOI, normal and inverted

That pattern recurs, with varying lag times, throughout the whole 144 year SOI history.

Which is why SSTs will probably increase to about February of 2021…

Figure 26:  Scaled SOI, normal and inverted, and detrended HadSST4

…and with them, CO2 concentration.

Figure 27:  Scaled SOI, normal and inverted, and detrended HadSST4, with South Pole CO2 data

This image has an empty alt attribute; its file name is soi-inv-sst-co2-1.jpg

Discussion

The long term carbon dioxide record shows continuing increase at all stations, indicating greater output than sinks can absorb. 

CO2 concentrations and trends, while similar, have discernible differences at different locations, notably between the hemispheres.

CO2 concentrations at Southern Hemisphere stations are increasing, but more slowly than those in the Northern Hemisphere, such that their trends are diverging.

On the long term CO2 rise are seasonal rises and falls, most likely due to seasonal vegetation, crop, and phytoplankton growth and decay. 

Peaks in CO2 concentration occur after winter and spring in both hemispheres- February to May at Barrow Point, April and May at Mauna Loa, and September-December at the South Pole.  This is not due however to a six month delay in CO2 mixing from sources in the Northern Hemisphere to the Southern, otherwise the South Pole trend would be the same.  It is lower, and becoming more so. 

There is great variety in seasonal range of CO2 at different locations, with greatest variation in the Arctic and the least in the Southern Hemisphere.

The amount and timing of these seasonal rises and falls varies from year to year.  These inter-year changes in CO2 concentrations can be as much as or greater than the normal annual increase.

Even though the South Pole station is far from the Southern Ocean, especially in winter when sea ice extends further, and even further from any vegetated land areas, its data appear representative of a great part of the Southern Hemisphere.

Small inter-annual changes in sea surface temperatures have a large impact on these changes in CO2 concentrations at South Pole and Mauna Loa about 12 to 24 months later.  There can be a further delay of up to five months in the effect at Point Barrow. 

This is not controversial.  According to the CSIRO, these variations “have been shown to correlate significantly with the regular El Niño-Southern Oscillation (ENSO) phenomenon and with major volcanic eruptions. These variations in carbon dioxide are small compared to the regular annual cycle, but can make a difference to the observed year-by-year increase in carbon dioxide.”

While sea surface temperature rise precedes CO2 concentration increase, there is no evidence at all of CO2 concentration change preceding sea surface temperature change.

With an apparent approximate 12 – 24 month delay between ocean temperature change and inter-annual CO2 change, changes in ocean out-gassing and absorption rates appears to be an unlikely mechanism.  Changes in land vegetation, forests, crops, and oceanic phytoplankton, moderated by the changing circulation, rainfall, cloud, and temperature patterns of ENSO events, appears to be a more likely mechanism, with the much smaller land area of the Southern Hemisphere accounting for the much smaller changes. 

The unresolved problem

This does not however explain the decreasing amount of summer draw down at the South Pole, and the divergence from Northern Hemisphere data.   Perhaps Southern Ocean phytoplankton are not decreasing as much during winter, so the CO2 sink is slightly increasing, slowing the CO2 growth trend a little and smoothing the CO2 growth curve.  Who knows?  I have yet to see a satisfactory- or any- explanation.

CO2vid Watch: May

June 8, 2020

I have been wondering 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.

Earlier 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.”

(In a coming post I will update my expectations for the end of the year and next year.) 

The CO2 concentration number for May is now published: 417.07 p.p.m. (parts per million).  That’s an increase of 0.86 ppm over the April figure, and 2.41 ppm above the figure for May last year.  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.

Fig. 1:  12 month change in CO2 concentration since 2015 to May 2020- Mauna Loa

Notice the amount of 12 month change has decreased a little.

Figure 2 is a monthly update for 2020 I will show as each month’s CO2 figures become available (and 2021 if necessary):

Fig. 2:  Updated 12 month changes in CO2 concentration for 2020- Mauna Loa

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

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

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.

Australia’s Wacky Weather Stations: Final Summary

January 16, 2020

(Updated 17/01/2020)

Australia, being a wealthy, modern, western nation with a very well-resourced Bureau of Meteorology (BOM), might be expected to have weather stations that set a high standard of siting and reliability.

Unfortunately, that is far from the case.  This pie chart shows the percentage of weather stations that comply with siting specifications, don’t meet those specifications, or are “marginal”- not fully compliant but not as bad as some.

Nearly half do not comply with siting guidelines as outlined in Observation Specification No. 2013.1 (drafted in January 1997).

Less than a third comply and may be relied on (assuming that the screen and the immediate area around it is kept well maintained with a few centimetres of natural grass, and the surrounding environment does not change).

The marginal stations may or may not be reliable.

In Australia it is apparently quite OK to have thermometers beside houses, in bitumen carparks, in a vegetable garden surrounded by a corrugated iron fence, beside incinerators, behind 6 metre prison walls, beside piles of human excrement, in the middle of a dirt road, on the roof of a wharf shed, beside a multi-lane highway, shaded by trees, or in screens that are covered in spider webs, invaded by mud wasps, or used by cattle as a back-scratcher.  The area around the screens can be dusty bare dirt, overgrown with grass and weeds, or sprayed out to bare ground.

This map, thanks to Lance Pidgeon, shows the locations of weather stations audited.

As you can see, removing non-compliant and marginal sites leaves very large gaps.

Australia’s climate analysis is based on 112 stations in the ACORN-SAT network.  I surveyed 111 Acorn stations.  (Wittenoom stopped reporting in July 2019 and is now apparently closed).  Here is a pie chart of Acorn station compliance:

Again, thanks to Lance, this is a map of Australian Acorn stations….

…and this map shows the layout of the Acorn network with non-compliant stations removed, leaving marginal and compliant sites.

Only New South Wales has a decent density of compliant sites.  There are huge gaps in Queensland, Western Australia, and South Australia.  No wonder the Bureau is desperately defending their realm!

Here are the non-compliant Acorn sites.

Adelaide (Kent Town)Mackay
Albany AirportMarble Bar
BarcaldineMarree Airport
BridgetownMelbourne (Olympic Park)
CamoowealMerredin
Cape BordaMildura
Cape BrunyMiles
Cape LeeuwinMorawa Airport
Cape MoretonMoruya Heads
Cape OtwayNuriootpa
Charters TowersPoint Perpendicular
Coffs Harbour AirportRichmond (Qld)
Cunderdin AirportRobe
DalwallinuRockhampton
DeniliquinRutherglen
Geraldton AirportScone Airport
GilesSnowtown
Halls CreekSydney – Observatory Hill
HobartTownsville
KalumburuWandering
KerangWilcannia Airport
KyancuttaWilsons Promontory
Larapuna (Eddystone Point)Woomera
LongreachYamba

Summary

Of 666 weather stations I was able to identify and survey, nearly half (328) did not comply with siting specifications.

Less than a third (209) fully comply (assuming that the screens and surroundings are well maintained).

Another 129 are marginal- not fully compliant but not as bad as the non-compliant sites.

48 of the 111 remaining Acorn stations are not compliant, and a further 22 are marginal.

The Bureau of Meteorology starts its climate analysis using Acorn from 1910.  Reasons given are that the network, especially in remote areas, and also Western Australia and Tasmania,  was extremely sparse before this, and except in Queensland and South Australia (where Clement Wragge had instituted Stevenson screens and standardised practises by the mid-1890s) temperature observations and instrument siting were non-standard.  Temperature records before 1910 are not recognised by the Bureau.  For example, the hottest temperature recorded, 53.1C at Cloncurry on 16 January 1889 is discounted as it was not recorded in a Stevenson screen; and the temperature of 51.6C in Bourke on 3 January 1909 is discounted, even though it was in a Stevenson screen.  How can we be any more confident in current temperatures recorded at non-compliant sites?

With only 209 stations of the 666 surveyed fully complying with specifications, doubt must be raised not only about the modern network coverage but also the reliability and comparability of modern and historical temperature records.

The next step:

Over some time, I will be comparing data from several pairs of compliant and non-compliant stations to see if siting has any detectable effect on temperatures recorded.

Appendix:- Background and details of survey: 

In July 2019 I commenced a 6 month long survey of 666 weather stations that currently report temperatures to the Latest Weather Observations pages for each state (also to Climate Data Online and to international weather and climate agencies).  Many are used to make adjustments to Acorn stations. Of the 753 stations listed (and these change from time to time) I was able to identify and examine 666. 

I did not include offshore island territories or islands far distant from the mainland (e.g. Willis island) but islands close to the coast were included.  Other stations not included were those in the National Tidal Centre network, which are located on wharves and breakwaters and have beehive screens instead of Stevenson screens; Lucas Heights nuclear facility; a number of recent defence stations that were impossible to locate; stations in areas where satellite imagery has poor resolution, and a number of sites that have not yet been included in the BOM metadata and thus have no site plans and can’t be located- a good example is Wellcamp Airport in southern Queensland. (See below for the full list.)

The process I used is outlined  in my post “How to check for yourself”.  I also made use of information and photographs supplied by colleagues with local knowledge.

There are 328 examples of stations that are not compliant with specifications, listed by state here.

This is an example of a compliant station:  Amberley AMO  40004 which is an Acorn station.

Google satellite image: 

This is an example of a marginal station:  Nullo Mountain 62100

Google satellite image: 

It has patches of rougher/ longer vegetation nearby and a large tree about 20 metres away.

(And the more I look at marginal sites the more I find that I should really have classified more as not compliant.)

These are the marginal stations (Acorn marked *):

Adelaide AirportMiddle Point
ArmidaleMilingimbi
BatchelorMoomba Airport
Batemans BayMortlake
Bathurst *Moss Vale
Boulia *Mount Boyce
Bourke *Mount Bundey North (Defence)
Bowen Airport AWSMount Crawford
Bradshaw-Angallari Valley (Defence)Mount Ginini
BrisbaneMount Ive
Brisbane Airport *Mount Magnet Airport
BulmanMount Moornapa
Burketown Airport *Mudgee
Cape GrimNambour
Cape SorellNeptune Island
Central Arnhem PlateauNew May Downs
CerberusNgayawili (Elcho Island)
CharltonNgukurr AWS
CombienbarNhill Aerodrome *
CooktownNoarlunga
CoonawarraNoonamah
CootamundraNullo Mountain
Cowley Beach (Defence)Oakey
Cultana (Defence)Oodnadatta *
Darwin Airport *Orbost *
DerbyPalmerville *
Devonport AirportParramatta
Dum In MirriePearce
DwellingupPort Augusta
Eildon Fire TowerPort Fairy
ElliottPortland Airport
Esperance AirportRedland (Alexandra Hills)
Essendon AirportRhyll
Eucla *Sheffield
FingalSheoaks
Forrest *Shepparton
Fowlers GapSt George *
Gabo Island *Stawell
GelantipyStenhouse Bay
Grafton AirportSwan Hill
Grove *Sweers Island
Hamilton IslandSydney Olympic Park
Horn Island *Tabulam
Hume ReservoirTarcoola *
Hunters HillTaree Airport
Jervis Bay AirfieldTemora
JervoisTennant Creek *
kunanyi / Mount WellingtonTerrey Hills
Kununurra AirportThargomindah *
Lake JuliusThe Monument
Lake Macquarie – CooranbongTibooburra Airport *
Lake St ClairTocal
Lancelin (Defence)Townsville Air Weapons Range (Defence)
Launceston Airport *Trepell
Laverton (WA)Tunnack
Legendre IslandTurretfield
Leigh CreekVictor Harbor
Leonora AirportWadeye (Port Keats)
LombadinaWarruwi
LostockWhite Cliffs AWS
Low Head *Windorah
Luncheon HillWoolbrook
Mackay AirportYanakie
McArthur River MineYoung
Melbourne Airport

These are the stations listed in Latest Weather Observations that I did not use (plus offshore island territories). You are welcome to try- let me know if you have any success.

Adele IslandMelville Water
Arlington ReefMiddle Percy Island
Banana BankMount Buller
Barrow IslandMount Bundey South (Defence)
Bedout IslandMount Hope
Bradshaw-Koolendong ValleyMount Hotham Airport
BrewonMount Keith
Broome PortMount Read
Browse IslandMulurulu
Bulga DownsNoona
Burnie PortNorth Head
Busselton JettyNorth Island
Canungra (Defence)Oberon
Cape FergusonOcean Reef
Cape WesselOuter Harbor (Black Pole)
Christmas CreekPaynes Find
Colpoys PointPoint Avoid
CoondewannaPort Kembla Harbour
Croker Island AirportPortland Harbour
Darwin HarbourPuckapunyal-Lyon Hill (Defence)
Degrussa AerodromeRosslyn Bay Harbour
DoonganRowley Shoals
Edi UpperSellicks Hill
Esperance HarbourSmithville
Evans HeadSolomon Airport
Fawkner BeaconSouth Channel Island
Fort DenisonSpitfire Channel
Fortescue Dave ForrestSpring Bay
FrankstonSt Kilda Harbour RMYS
GluepotSwan Island
Gooseberry HillSydney Harbour
Hay PointThevenard Island
Heron IslandThursday Island
Hillarys Point Boat HarbourTownsville – Fanning River (Defence)
Hindmarsh IslandTroughton Island
Inner BeaconUrandangi
Kingfish BVaranus Island
KurnellWarburto Point
Little BayWarburton
LochingtonWattamolla
Low Rocky PointWellcamp Airport
Lucas Heights (ANSTO)West Roebuck
Maatsuyker IslandYampi Sound (Defence)
Maitland Airport

The Wacky World of Weather Stations by State

January 12, 2020

Here are 328 Australian weather stations that are not compliant with Bureau of Meteorology specifications for siting.

Site compliance is important because temperature data from these stations is liberally reported in the media especially if hot or cold records are set. They also contribute to AWAP (the Australian Water Availability Project) and ADAM which produce maps of present and past temperatures. As well, data from these stations is used to homogenise data at stations in Australia’s ACORN-SAT network, which are used for showing trends since 1910. Finally, this data is exported to be used by international databases (GISSCruTem4) for regional and global climate analysis. If the data is affected by site specific factors, e.g being too close to a road, this may affect the quality of the analysis.

I am agnostic as to the overall effect of these poorly sited stations. Maxima at some sites may be artificially low, or minima may be artificially high, and this may vary with seasons and rainfall. Analysis and comparison of temperatures will come later. My focus here is to show the large number of modern stations whose data may be unreliable.

The quality of these non-compliant stations varies. While some are truly horrendous, others are not nearly as bad, but all fail on one or more specifications.

Find your favourite station, click on it, and the link will take you to my assessment of its siting quality. Use the back arrow to return here.

South AustraliaVictoria
Adelaide (SA)Aireys Inlet (Vic)
Andamooka (SA)Ararat (Vic)
Arkaroola (SA)Avalon (Vic)
Cape Borda (SA)Bairnsdale (Vic)
Cape Willoughby (SA)Ben Nevis (Vic)
Cleve (SA)Cape Nelson (Vic)
Cleve Airport (SA)Cape Otway (Vic)
Cummins Airport (SA)Castlemaine (Vic)
Edinburgh (SA)Coldstream (Vic)
Edithburgh (SA)Dartmouth (Vic)
Elliston (SA)Echuca Airport (Vic)
Eudunda (SA)Falls Creek (Vic)
Kadina (SA)Ferny Creek (Vic)
Karoonda (SA)Horsham (Vic)
Keith (SA)Kerang (Vic)
Kimba (SA)Lake Eildon (Vic)
Kingscote (SA)Lakes Entrance (Vic)
Kuitpo (SA)Latrobe Valley  (Vic)
Kyancutta (SA)Longerenong (Vic)
Lameroo (SA)Maryborough (Vic)
Loxton (SA)Melbourne (Vic)
Maitland (SA)Mildura (Vic)
Marree (SA)Moorabbin (Vic)
Meningie (SA)Mount Nowa Nowa (Vic)
Minlaton Aero (SA)Mount William (Vic)
Minnipa (SA)Omeo (Vic)
Mount Barker (SA)Point Hicks (Vic)
Mount Lofty (SA)Redesdale (Vic)
Murray Bridge (SA)Rutherglen (Vic)
Naracoorte (SA)Scoresby (Vic)
Nullarbor (SA)Tatura (Vic)
Nuriootpa (SA)Viewbank (Vic)
Padthaway (SA)Walpeup (Vic)
Pallamana (SA)
Price (SA)Wangaratta (Vic)
Robe (SA)Warracknabeal (Vic)
Roseworthy (SA)Warragul (Vic)
Snowtown (SA)Warrnambool (Vic)
Streaky Bay (SA)Westmere (Vic)
Warooka (SA)Wilsons Promontory (Vic)
Whyalla (SA)Wonthaggi (Vic)
Woomera (SA)
Yongala (SA)Tasmania
Bushy Park (Tas)
Northern TerritoryCape Bruny (Tas)
Black Point (NT)Cape Bruny AWS (Tas)
Centre Island  (NT)Cressy (Tas)
Charles Point (NT)Dover (Tas)
Curtin Springs (NT)Flinders Island Airport (Tas)
Jabiru (NT)Friendly Beaches (Tas)
Kangaroo Flats (NT)Hartz Mountain (Tas)
Kintore (NT)Hobart (Tas)
Kulgera (NT)Lake Leake (Tas)
Lajamanu Airport (NT)Larapuna (Eddystone Pt) (Tas)
Murganella Airstrip (NT)Launceston (Tas)
Pirlangimpi (NT)Marrawah (Tas)
Point Fawcett (NT)Maydena (Tas)
Point Stuart (NT)Orford (Tas)
Territory Grape Farm (NT)Ouse (Tas)
Yulara Airport (NT)Scotts Peak (Tas)
Strahan (Tas)
Warra (Tas)
Wynyard (Tas)
West AustraliaNew South Wales
Albany (WA)Albury (NSW)
Albany Airport (WA)Balranald (NSW)
Argyle (WA)Bankstown (NSW)
Badgingarra (WA)Barraba (NSW)
Barimunya (WA)Bathurst Airport (NSW)
Beverley (WA)Bombala (NSW)
Bickley (WA)Bombala AWS (NSW)
Bidyadanga (WA)Brewarrina (NSW)
Bridgetown (WA)Burrinjuck Dam (NSW)
Busselton (WA)Cape Byron (NSW)
Cape Leeuwin (WA)Cobar Airport (NSW)
Cape Naturaliste (WA)Coffs Harbour (NSW)
Carnamah (WA)Collarenebri (NSW)
Carnegie (WA)Coonabarabran (NSW)
Collie East (WA)Coonabarabran Airport (NSW)
Corrigin (WA)Cowra (NSW)
Cunderdin (WA)Deniliquin (NSW)
Cygnet Bay (WA)Dunedoo (NSW)
Dalwallinu (WA)Forster (NSW)
Denham (WA)Grafton Airport (NSW)
Donnybrook (WA)Green Cape (NSW)
Eyre (WA)Grafton AgRS (NSW)
Geraldton Airport (WA)Griffith (NSW)
Giles (WA)Gulgong (NSW)
Goomalling (WA)Gundagai (NSW)
Gosnells City (WA)Guyra (NSW)
Halls Ck (WA)Hillston (NSW)
Hopetoun North (WA)Holsworthy (Defence) (NSW)
Hyden (WA)Katoomba (NSW)
Jacup (WA)Lake Cargelligo (NSW)
Jarrahwood (WA)Lake Victoria (NSW)
Jurien Bay (WA)Lightning Ridge (NSW)
Kalbarri (WA)Lismore Airport (NSW)
Kalumburu (WA)Mangrove Mountain (NSW)
Karijini North (WA)Menindee (NSW)
Karratha (WA)Merimbula (NSW)
Kellerberrin (WA)Montague Island (NSW)
Lake Grace (WA)Moruya Heads (NSW)
Lancelin (WA)Mount Seaview (NSW)
Leinster (WA)Mungindi  (NSW)
Mandora (WA)Murrurundi (NSW)
Mandurah (WA)Murwillumbah (NSW)
Manjimup (WA)Narooma (NSW)
Marble Bar (WA)Nelson Bay (NSW)
Merredin (WA)Newcastle (NSW)
Millendon (WA)Newcastle Univ. (NSW)
Morawa (WA)Norah Head (NSW)
Mount Elizabeth (WA)Nyngan (NSW)
Mount Barker (WA)Parkes (NSW)
Mullewa (WA)Peak Hill (NSW)
Munglinup West (WA)Penrith (NSW)
Murchison (WA)Perisher Valley (NSW)
Narembeen (WA)Pindari Dam (NSW)
Narrogin (WA)Pooncarrie (NSW)
Newman Aero (WA)Point Perpendicular (NSW)
Northam (WA)Quirindi (NSW)
Ongerup (WA)Scone (NSW)
Pemberton (WA)Singleton (NSW)
Perth Metro (WA)Smoky Cape (NSW)
Pingelly (WA)Springwood (NSW)
Ravensthorpe (WA)Sydney  (NSW)
Red Rocks Point (WA)Sydney Airport (NSW)
Salmon Gums RS (WA)Taralga  (NSW)
Shark Bay Airport (WA)Tenterfield (NSW)
Southern Cross (WA)Thredbo Village (NSW)
Swanbourne (WA)Tocumwal (NSW)
Telfer (WA)Tumbarumba (NSW)
Wagin (WA)Ulladulla (NSW)
Warmun (WA)Wanaaring (NSW)
Wiluna (WA)Wellington (NSW)
Witchcliffe (WA)Wilcannia Airport (NSW)
Wongan Hills (WA)Yamba (NSW)
York (WA)Yanco (NSW)
Wandering (WA)
Queensland
Alva Beach (Qld)Low Isles (Qld)
Applethorpe (Qld)Lucinda (Qld)
Ayr DPI (Qld)Mackay MO (Qld)
Barcaldine (Qld)Mareeba (Qld)
Beaudesert (Qld)Miles (Qld)
Bedourie (Qld)Mitchell (Qld)
Bollon (Qld)Mornington Is Airport (Qld)
Cape Flattery (Qld)Mount Isa  (Qld)
Cape Moreton (Qld)Mount Stuart Defence (Qld)
Camooweal (Qld)Point Lookout (Qld)
Cardwell (Qld)Rainbow Beach (Qld)
Century Mine (Qld)Redcliffe(Qld)
Charters Towers (Qld)Richmond (Qld)
Coconut Island (Qld)Rockhampton (Qld)
Collinsville (Qld)Rolleston (Qld)
Cunnamulla (Qld)Rundle Island (Qld)
Double Island Point (Qld)Sandy Cape (Qld)
Emerald (Qld)Seventeen Seventy (Qld)
Gladstone Airport (Qld)Springsure (Qld)
Gladstone Radar (Qld)Stanthorpe (Qld)
Gold Coast Seaway (Qld)South Johnstone (Qld)
Greenbank (Qld)Sunshine Coast Airport (Qld)
Gympie (Qld)Surat (Qld)
Hervey Bay (Qld)Tambo (Qld)
Ingham (Qld)Taroom (Qld)
Injune (Qld)Tewantin RSL Park (Qld)
Innisfail (Qld)Texas (Qld)
Innisfail Aero (Qld)Tin Can Bay (Qld)
Kowanyama Airport (QLD)Townsville (Qld)
Lady Elliott Island (Qld)Woolshed (Qld)
Logan City (Qld)Yeppoon (Qld)
Longreach (Qld)

The Wacky World of Weather Stations: No. 328- Ferny Creek (Vic)

January 10, 2020

Friday 10/01/2020

Please refer back to my first post for site specifications and to No. 92- Logan City for 2018 specifications.  If you wish to check on this (or any) site for yourself, go to my post on how to check for yourself.

Station: Ferny Creek 86266

Opened: 2011

Daily Temperature data from: 2011

Data used to adjust Acorn sites at: —

Co-ordinates: -37.8748 145.3496

35km east of Melbourne CBD.

BOM site plan 2018:

Google satellite image:

Google ‘street’ view 2019 (photo Glenn Batchelor):

Drone photo:

The screen is on sloping land and is a few metres from shrubs; a 50m tall tree is about 40 metres away.

This station is non-compliant, with temperatures reported at Latest Weather Observations  but not used to adjust data at Acorn sites.

FAIL

Percentage of all sites non-compliant: 49.25%.

That’s it: I think I’ve finished. Next I’ll update the index.

The Wacky World of Weather Stations: No. 327- Lake Grace (WA)

January 10, 2020

Friday 10/01/2020

Please refer back to my first post for site specifications and to No. 92- Logan City for 2018 specifications.  If you wish to check on this (or any) site for yourself, go to my post on how to check for yourself.

Station: Lake Grace 10911

Opened: 1997

Daily Temperature data from: 1997

Data used to adjust Acorn sites at: Esperance

Co-ordinates: -33.1006 118.4647

275km south-east of Perth

BOM site plan 2015:

Google satellite image:

Google street view 2008:

The screen is 15 metres from a bitumen road, 36 metres from a bitumen carpark, and 18 metres from the railway track. It is also close to bare dirt. Further, in 2008, either the area was watered or else the natural grass was worn away or removed to bare dirt to within a few metres.

This station is non-compliant, with temperatures reported at Latest Weather Observations  and used to adjust data at Acorn sites.

FAIL

Percentage of all sites non-compliant: 49.1%.

The Wacky World of Weather Stations: No. 326- Albany Airport (WA)

January 10, 2020

Friday 10/01/2020

Please refer back to my first post for site specifications and to No. 92- Logan City for 2018 specifications.  If you wish to check on this (or any) site for yourself, go to my post on how to check for yourself.

Station: Albany Airport 9999

Opened: 2012

Daily Temperature data from: 2012

Data used to adjust Acorn sites at: —

Co-ordinates: -34.9411 117.8158

380km south-east of Perth

BOM site plan 2011:

Google satellite image:

This Acorn station was established here in 2012, but there have been no site plans since 2011. For a period of time until the grass grew, the enclosure was “sand”- similar to the bare areas beside the concrete path. Like many airports, it is on a raised mound of sand 60cm above the surroundings, so the screen in effect is 1.8 metres above natural ground level.

This station is non-compliant, with temperatures reported at Latest Weather Observations  but not used to adjust data at Acorn sites.

FAIL

Percentage of all sites non-compliant: 48.95%.

The Wacky World of Weather Stations: No. 325- Witchcliffe (WA)

January 9, 2020

Thursday 09/01/2020

Please refer back to my first post for site specifications and to No. 92- Logan City for 2018 specifications.  If you wish to check on this (or any) site for yourself, go to my post on how to check for yourself.

Station: Witchcliffe 9746

Opened: 1999

Daily Temperature data from: 1999

Data used to adjust Acorn sites at: —

Co-ordinates: -34.0281 115.1042

230km south of Perth

BOM site plan 2018:

Google satellite image:

This station is in the middle of a vineyard, surrounded by irrigated grape vines which are periodically pruned then grow luxuriantly to a couple of metres high. The screen will thus be shielded by the vines. See Nuriootpa.

This station is non-compliant, with temperatures reported at Latest Weather Observations  but not used to adjust data at Acorn sites.

FAIL

Percentage of all sites non-compliant: 48.8%.

The Wacky World of Weather Stations: No. 324- Badgingarra (WA)

January 9, 2020

Thursday 09/01/2020

Please refer back to my first post for site specifications and to No. 92- Logan City for 2018 specifications.  If you wish to check on this (or any) site for yourself, go to my post on how to check for yourself.

Station: Badgingarra Research Station 9037

Opened: 1962

Daily Temperature data from: 1965

Data used to adjust Acorn sites at: Morawa

Co-ordinates: -30.3381 115.5394

180km north of Perth

BOM site plan 2013:

Google satellite image:

An ideal rural location? Not so. The screen is close to a tall crop in large clumps, and there is another growing crop across the road. The surrounding vegetation changes as crops grow and are harvested. As well the enclosure appears not to be well maintained grass to a few centimetres.

This station is non-compliant, with temperatures reported at Latest Weather Observations  and used to adjust data at Acorn sites.

FAIL

Percentage of all sites non-compliant: 48.65%.

The Wacky World of Weather Stations: No. 323- Red Rocks Point (WA)

January 9, 2020

Thursday 09/01/2020

Please refer back to my first post for site specifications and to No. 92- Logan City for 2018 specifications.  If you wish to check on this (or any) site for yourself, go to my post on how to check for yourself.

Station: Red Rocks Point 11053

Opened: 1999

Daily Temperature data from: 1999

Data used to adjust Acorn sites at: —

Co-ordinates: -32.2028 127.5297

1,100km east of Perth, 1,070 west of Adelaide.

BOM site plan 2019:

Google satellite image:

The screen is on sand within a couple of metres of coastal shrubs to 2 metres.

This station is non-compliant, with temperatures reported at Latest Weather Observations but not used to adjust data at Acorn sites.

FAIL

Percentage of all sites non-compliant: 48.5%.

The Wacky World of Weather Stations: No. 322- Shark Bay Airport (WA)

January 9, 2020

Thursday 09/01/2020

Please refer back to my first post for site specifications and to No. 92- Logan City for 2018 specifications.  If you wish to check on this (or any) site for yourself, go to my post on how to check for yourself.

Station: Shark Bay Airport 6105

Opened: 2000

Daily Temperature data from: 2000

Data used to adjust Acorn sites at: —

Co-ordinates: –25.8925 113.5772

710km north-west of Perth.

BOM site plan 2017:

Google satellite image:

The screen is on bare red dirt and less than 5 metres from 3 metre bushes.

This station is non-compliant, with temperatures reported at Latest Weather Observations but not used to adjust data at Acorn sites.

FAIL

Percentage of all sites non-compliant: 48.35%.

The Wacky World of Weather Stations: No. 321- Telfer (WA)

January 9, 2020

Thursday 09/01/2020

Please refer back to my first post for site specifications and to No. 92- Logan City for 2018 specifications.  If you wish to check on this (or any) site for yourself, go to my post on how to check for yourself.

Station: Telfer Aero 13030

Opened: 1974

Daily Temperature data from: 1974

Data used to adjust Acorn sites at: Marble Bar, Port Hedland, Wittenoom

Co-ordinates: -21.7125 122.2281

1,300km north-east of Perth.

BOM site plan 2018:

Google satellite image:

The screen is close to bare red dirt, dry and dusty for most of the year. Vehicles drive close to the screen.

This station is non-compliant, with temperatures reported at Latest Weather Observations and used to adjust data at Acorn sites.

FAIL

Percentage of all sites non-compliant: 48.2%.

The Wacky World of Weather Stations: No. 320- Karratha (WA)

January 9, 2020

Thursday 09/01/2020

Please refer back to my first post for site specifications and to No. 92- Logan City for 2018 specifications.  If you wish to check on this (or any) site for yourself, go to my post on how to check for yourself.

Station: Karratha Aero 4083

Opened: 1971

Daily Temperature data from: 1993

Data used to adjust Acorn sites at: Marble Bar, Port Hedland, Wittenoom

Co-ordinates: -20.7097 116.7742

1,250km north of Perth.

BOM site plan 2018:

Google satellite image:

Google wider view:

Wider still:

The screen is on bare red dirt, dry and dusty for most of the year. The wider views show the extensive areas of paved aprons and car parks, and then the salt evaporation pans beyond. Not a good site.

This station is non-compliant, with temperatures reported at Latest Weather Observations and used to adjust data at Acorn sites.

FAIL

Percentage of all sites non-compliant: 48.05%.

The Wacky World of Weather Stations: No. 319- Argyle (WA)

January 9, 2020

Thursday 09/01/2020

Please refer back to my first post for site specifications and to No. 92- Logan City for 2018 specifications.  If you wish to check on this (or any) site for yourself, go to my post on how to check for yourself.

Station: Argyle Aerodrome 2064

Opened: 1986

Daily Temperature data from: 1994

Data used to adjust Acorn sites at: Halls Creek, Victoria River Downs

Co-ordinates: -16.6380 128.4516

580km south-west of Darwin, 680km north-east of Broome.

BOM site plan 2019:

Google satellite image:

The site plan is incorrect, the apron is at least 80 metres from the screen. However, the screen is on an area of bare dirt.

This station is non-compliant, with temperatures reported at Latest Weather Observations and used to adjust data at Acorn sites.

FAIL

Percentage of all sites non-compliant: 47.9%.

The Wacky World of Weather Stations: No. 318- Centre Island (NT)

January 8, 2020

Wednesday 08/01/2020

Please refer back to my first post for site specifications and to No. 92- Logan City for 2018 specifications.  If you wish to check on this (or any) site for yourself, go to my post on how to check for yourself.

Station: Centre Island 14703

Opened: 1968

Daily Temperature data from: 1974

Data used to adjust Acorn sites at: —-

Co-ordinates: -15.7426 136.8192

740km south-east of Darwin

BOM site plan 2017:

Google satellite image:

The screen is far too close to trees.

This station is non-compliant, with temperatures reported at Latest Weather Observations but not used to adjust data at Acorn sites.

FAIL

Percentage of all sites non-compliant: 47.75%.

The Wacky World of Weather Stations: No. 317- Murganella Airstrip (NT)

January 8, 2020

Wednesday 08/01/2020

Please refer back to my first post for site specifications and to No. 92- Logan City for 2018 specifications.  If you wish to check on this (or any) site for yourself, go to my post on how to check for yourself.

Station: Murganella Airstrip 14309

Opened: 2012

Daily Temperature data from: 2012

Data used to adjust Acorn sites at: —-

Co-ordinates: -11.5485 132.9266

250km north-east of Darwin

BOM site plan 2018:

Google satellite image:

The screen is 4 metres from the bare dirt “cleared area” which surrounds the enclosure (which appears to have rough grass longer than a few centimetres.)

This station is non-compliant, with temperatures reported at Latest Weather Observations but not used to adjust data at Acorn sites.

FAIL

Percentage of all sites non-compliant: 47.6%.

The Wacky World of Weather Stations: No. 316- Black Point (NT)

January 8, 2020

Wednesday 08/01/2020

Please refer back to my first post for site specifications and to No. 92- Logan City for 2018 specifications.  If you wish to check on this (or any) site for yourself, go to my post on how to check for yourself.

Station: Black Point 14153

Opened: 1965

Daily Temperature data from: 1990

Data used to adjust Acorn sites at: Darwin

Co-ordinates: -11.1538 132.1430

200km north-east of Darwin

BOM site plan 2017:

Google satellite image:

The screen is right beside a dirt track, and 8 to 15 metres from 8m to 10m tall trees.

This station is non-compliant, with temperatures reported at Latest Weather Observations and used to adjust data at Acorn sites.

FAIL

Percentage of all sites non-compliant: 47.45%.

The Wacky World of Weather Stations: No. 315- Redcliffe (Qld)

January 8, 2020

Wednesday 08/01/2020

Please refer back to my first post for site specifications and to No. 92- Logan City for 2018 specifications.  If you wish to check on this (or any) site for yourself, go to my post on how to check for yourself.

Station: Redcliffe 40958

Opened: 2003

Daily Temperature data from: 2004

Data used to adjust Acorn sites at: —

Co-ordinates: -27.2169 153.0922

29km north of Brisbane

BOM site plan 2018:

Google satellite image:

The screen is 15 metres from a building and a bitumen path. The grass in the enclosure casts a shadow so must be much more than a few centimetres tall. Like so many others, this site is neglected and overgrown.

This station is non-compliant, with temperatures reported at Latest Weather Observations and used to adjust data at Acorn sites.

FAIL

Percentage of all sites non-compliant: 47.3%.

The Wacky World of Weather Stations: No. 314- Mount Isa (Qld)

January 8, 2020

Wednesday 08/01/2020

Please refer back to my first post for site specifications and to No. 92- Logan City for 2018 specifications.  If you wish to check on this (or any) site for yourself, go to my post on how to check for yourself.

Station: Mount Isa Aero 29127

Opened: 1966

Daily Temperature data from: 1966

Data used to adjust Acorn sites at: Boulia, Camooweal, Richmond (Qld), Tennant Creek

Co-ordinates: -20.6778 139.4875

1,565km north-west of Brisbane

BOM site plan 2016:

Google satellite image:

The screen is on bare dusty soil.

This station is non-compliant, with temperatures reported at Latest Weather Observations and used to adjust data at Acorn sites.

FAIL

Percentage of all sites non-compliant: 47.15%.