Posts Tagged ‘temperature’

Solar Exposure

June 6, 2018

The Bureau of Meteorology publishes many useful datasets on its Climate Data Online portal, including one minute solar exposure data for selected sites around Australia.  You have to register to receive monthly data here.

(In contrast with their one minute temperature data which are not available at CDO but must be requested and purchased, and are really “final second of each minute”, their solar exposure data are (a) free, and (b) include for each minute, maximum 1 second irradiance, minimum 1 second irradiance, and THE MEAN IRRADIANCE FOR THE PREVIOUS 60 SECONDS.  Why not temperature?  We can only wonder.  But I digress.)

I am naturally curious and enjoy finding out new stuff, so in this post I’ll show a number of plots for the months of July 2017, December 2017, and February 2018 to illustrate some things I’ve found about summer and winter solar exposure for Rockhampton.  Why Rocky?  It’s where I live, and is just a few kilometres north of the Tropic of Capricorn.  At the end of December the sun is directly overhead, so December shows interesting information.  February is typically the wettest and cloudiest month, and July usually the coldest and driest.

One minute solar exposure data have several components:  direct (normal) irradiance (rate of energy from the direct beam of the sun tracked throughout the day); direct horizontal irradiance (the amount striking a horizontal surface); diffuse irradiance (radiation scattered from the atmosphere including dust and clouds striking a horizontal surface); and “global” irradiance which is the sum of the horizontal and diffuse components.  Also measured is “terrestrial” irradiance, which is downwards infra-red radiation on a horizontal surface, and related to the temperature of the atmosphere, including from clouds and humidity (not just at ground level, but throughout the troposphere).

Figure 1:  Irradiance for February 2018

rocky all feb 18

Note that terrestrial (infra-red) irradiance is fairly constant at around 350-450 watts per square metre, while direct irradiance on a horizontal surface fluctuates from zero to ~1000 W/sq.m., and diffuse irradiance fluctuates from zero to ~900 W/sq.m.  For a closer look here are the same data for one day, 1st February:

Figure 2:  Irradiance for 1 February 2018

rocky all 1 feb 18

Mean horizontal irradiance (the direct beam from the sun on a horizontal surface) is zero in the absence of direct sunlight- at night, but also when clouds are thick enough, and also is greatly reduced even by thinner cloud; at other times, it rises rapidly to ~900 W/sq.m. at noon.

Diffuse irradiance is zero until a few minutes before sunrise, with radiation reflecting from clouds, dust, and other atmospheric particles; similarly just after sundown.  It is much higher in cloudy conditions.

IR irradiance, relatively constant before sunrise at ~400 W/sq.m., rises during the day as the atmosphere warms.  It also fluctuates with cloudy conditions, more noticeably at night.  Clouds are composed of water droplets and emit IR radiation- a natural greenhouse effect.

The next plot shows how irradiance varies over four days as clouds and rain increase.

Figure 3:  Irradiance for 1 – 4 February 2018

rocky all 1 to 4 feb 18

The effect of cloud on horizontal irradiance is obvious.  Diffuse irradiance is maximised on the 3rd; on the 4th, clouds reflect most solar radiation, the surface is cool, and IR irradiance which had increased due to cloudiness on the 2nd and 3rd, returns to ~400 W/sq.m.

By contrast, Figure 4 shows irradiance during the hottest week of February with maxima above 39.1C (41.1C on the 12th).

Figure 4:  Irradiance for 11 – 15 February 2018

rocky all 11 to 15 feb 18

Note the smooth curves of horizontal and diffuse irradiance on 11th and 12th; early morning cloud on 13th – 15th with diffuse and IR increasing; and IR increases with surface temperature, peaking in the late afternoon- with little surges as clouds pass overhead.

Figure 5 shows the variation of IR irradiance during February.

Figure 5:  IR Irradiance for February 2018

rocky IR feb 18

The diurnal fluctuation typically of 60-70 W/sq.m. is obvious, as is the change over time.  The bottom of the daily fluctuation occurs in the early morning.  Notice the effect on the minimum temperature:

Figure 6:  Minima for February 2018

Tmin Feb 18

The last plot for February shows the irradiance from the direct beam of the sun tracked throughout the day:

Figure 7:  Direct Irradiance for February 2018

rocky direct feb 18

It’s interesting that the irradiance of the direct beam is not constant, even on clear sunny days.  It is possible that the rain of the first four days removed suspended particles; from 5th to 9th the wind was from the east or south-east (from the sea); from the 11th to 15th it was from the north west to north, blowing dust and smoke from the land, resulting in slightly dimmer conditions.

I now turn to July 2017.  July is usually the coolest and driest month in Rockhampton.

Figure 8:  Irradiance for July 2017

rocky all july 17

Due to the much lower solar angle, horizontal irradiance is much lower than February, mostly from 600 to 700 W/sq.m.  IR irradiance is more variable, so needs a closer look.

Figure 9:  Irradiance for 6 – 10 July 2017

rocky all 6 to 10 july 17

These were cloudy days, with wind from the north-west on the 6th to 8th, with a south-east change on the 9th with light rain on 9th and 10th.

19th to 22nd shows more of this atypical winter weather.

Figure 10:  Irradiance for 19 – 22 July 2017

rocky all 19 to 22 july 17

Overcast and 90% Relative Humidity in the morning of the 19th, then RH fell rapidly, with the lowest 3:00 p.m. reading for the month (16%) and 9:00 a.m. (36%) on the afternoon of the 21st and the morning of the 22nd– when IR, and minimum temperature, were lowest for the month.  The 20th and 21st were clear sunny days.   Some cloud arrived on the afternoon of the 22nd.

Figure 11:  Irradiance for 25 – 28 July 2017

rocky all 25 to 28 july 17

This is typical winter weather- clear skies, cool nights followed by warm sunny days.  Note the smooth curves for horizontal and diffuse irradiance, both much less than February.  This indicates cloudless skies and low humidity.  There is a little early morning fog or mist as indicated by small wiggles in IR irradiance, but not enough to affect diffuse irradiance.  IR irradiance again peaks in mid afternoon.

Figure 12:  IR Irradiance for July 2017

rocky IR july 17

Due to less direct irradiance, cooler temperatures, and lower humidity, IR irradiance is much lower than in February, and rarely exceeds 400 W/sq.m.  IR fluctuates less in clear dry conditions.   Again, IR is reflected in minima:

Figure 13:  Minima for July 2017

Tmin July 17

Figure 14:  Direct Irradiance for July 2017

rocky direct july 17

Note that direct irradiance is not much less than in February, even for being soon after aphelion: it is the sun’s lower angle in the sky that makes most of the difference.  The clear dry days on the 20th and 21st have the highest irradiance.

The next plots are for December, around summer solstice and close to perihelion, when days are typically hot and sultry.

Figure 15:  Irradiance for December 2017

rocky all dec 17

The first four days, and the 9th, were cloudy, with rain on 3rd and 4th, as you can see from the horizontal irradiance.  On the remaining days irradiance was close to 1000 W/sq.m.

Figure 16:  IR Irradiance for December 2017

rocky IR dec 17

Heavy cloud, swept in from the Coral Sea, on the first four days, and hotter maxima on the last two, pushed IR well above 400W/sq.m.

And the plot for minima:

Figure 17:  Minima for December 2017

Tmin Dec 17

Last one!

Figure 18:  Direct Irradiance for December 2017

rocky direct dec 17

You will notice that with the sun virtually directly overhead around noon each day (from 1.56 degrees from zenith on 1st December to 0.01 degrees from zenith on Christmas Day), sun tracking direct irradiance is almost the same as the horizontal irradiance.

What have I learnt?  The variability of solar exposure, which is strongly affected by what’s in the atmosphere: dust, smoke, gaseous water, liquid water (clouds); as well as time of year and time of day.  The extent that downwards infra-red irradiance, which is an indicator of atmospheric temperature, is increased by daytime surface temperature and also very noticeably by clouds, and decreased by lower humidity.  How IR strongly influences minima- the greenhouse effect.

Nothing new probably, but I hope you found it as interesting as I did.

Finally:  why, oh why, can’t the Bureau make one minute temperature data freely available, and why does it persist with one second temperature readings rather than the mean over the previous minute, which it calculates with solar exposure?

My next post will look at different factors influencing temperature, including solar exposure.

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The Chicken or the Egg?

May 3, 2018

Climate scientists assert that increasing concentrations of carbon dioxide and other greenhouse gases in the atmosphere have caused and will continue to cause global temperature to increase.  Real world evidence to support this is sadly lacking.

I use CO2 data from NOAA at Mauna Loa and HadSST3  Sea Surface data to compare both over the same period, as oceans cover most of global surface.

There have been 60 years of continued and accelerating CO2 increase.

Figure 1: 60 years of carbon dioxide concentration

CO2 abs trend

Ocean temperatures have also increased:

Figure 2:  HadSST3 Sea Surface Temperature from 1958

Hadsst3

While you may note the distinct lack of warming before the mid 1970s, and that although a quadratic trend line fits the data, the increase is not smooth but a series of steps with some large spikes at about the time of ENSO events, climate scientists insist that it is the overall trend that is important.

The following plot appears to support the greenhouse warming theory.

Figure 3:  Global Sea Surface Temperature anomalies as a function of CO2 concentration

SST vs CO2

It seems that nearly three quarters of the temperature change since 1958 can be explained by the increase in CO2 concentration.  This accords with the theory.

But what if we reverse the axes in Figure 3?

Figure 4:  CO2 concentration as a function of Sea Surface Temperature anomalies

CO2 vs SST

It is equally valid to propose that nearly three quarters of the increase in carbon dioxide concentration can be explained by increasing sea surface temperatures, although that is not the point of this exercise.

To determine if CO2 is the cause of increasing temperature, or vice versa, we need to compare SST anomalies and CO2 concentration as a function of time.  If SST and CO2 both change at the same time, we are no further advanced, but if CO2 changes before SST (due to thermal inertia of the oceans), then that would be evidence for CO2 increase being the driver of temperature increase.

Both CO2 concentration and SST anomalies have pronounced trends, so for comparison both datasets are detrended, and the large seasonal signal is removed from CO2 data to calculate monthly “anomalies”.

Remember, it is increasing CO2 which is supposed to cause increasing temperature, not a static amount, so change in CO2 and SST must be our focus.

My measure of change in SST and CO2 is 12 monthly difference: for example January 2000 minus January 1999.  The next plot shows 12 monthly difference in both SST and CO2 anomalies from 1959 to 2018.  (SST is scaled up for comparison).

Figure 5:  12 monthly change in detrended SST and CO2 anomalies

12m chg Hadsst3 co2

SST appears to spike before CO2.  In the next plot, SST data have been lagged by seven months:

Figure 6:  12 monthly change in detrended SST (lagged 7 months) and CO2 anomalies

lagged 7m 12m chg Hadsst3 co2

There appear to be differences in some decades- the lag time varies from four months to eight or nine months.

Here’s the plot of CO2 vs lagged SST:

Figure 7:  12 month change in CO2 as a function of 12 month change in SST, lagged 7 months

lagged 12m SST vs CO2

Correlation co-efficient of 0.57 is not bad considering we are comparing all ocean basins and the atmosphere.

As SST change generally precedes CO2 change by about seven months (sometimes less, sometimes more), there is NO evidence that CO2 increase causes temperature increase.

But we are still left with the increase in CO2 from 1958 while SST paused or decreased for 19 years.

Figure 8:  Sea Surface Temperature and CO2 concentration, 1958-1976

Hadsst and CO2 58 76

While it is difficult to attribute decadal CO2 increase to non-existent SST rise, there is no evidence for CO2 driving temperature increase in this period.

However, plotting 12 month change of CO2 and SST clearly reveals their relationship.

Figure 9: 12 month change in detrended CO2 and SST anomalies

12m chg Hadsst and CO2 58 76

Figure 10: 12 month change in detrended CO2 and SST anomalies, lagged 7 months

lagged 12m chg Hadsst and CO2 58 76

It is clear that 12 monthly change in temperature drives 12 monthly change in CO2 concentration.

The continual rise in CO2 from 1958 to 1976 while SST declined indicates there must be an underlying increase in CO2 unrelated to immediately preceding temperature, but there is definitely no evidence that it causes sea surface temperature increase at any time.

Summary:

  1. Increase in CO2 concentration is supposed to be the cause of the increase in temperature we see in the SST data (and satellite data).
  2. However, analysis shows that CO2 changes about four to seven months (and longer) after sea surface temperature changes.
  3. Therefore, atmospheric CO2 increase cannot be the cause of surface temperature increase. Real world data disproves the theory.

UAH, ACORN and Rainfall: Something’s Wrong

April 4, 2018

Tom Quirk had an interesting article posted by Jo Nova this week, at

http://joannenova.com.au/2018/04/bom-homogenization-errors-are-so-big-they-can-be-seen-from-space/

questioning the large number of adjustments coincident with the changeover to automatic weather stations in the 1990s, which appear to have had a large impact on the correlation between BOM’s monthly ACORN mean temperatures and UAH’s Lower Troposphere data for the Australian region.

However, using a different comparison something very strange appears.

For me, his killer plot was this one, showing a huge drop in centred running 13 month correlations between UAH and BOM mean anomalies:

Figure 1: Tom’s plot of monthly correlations:

Tom Q correl plot

Using the same methodology, but with maxima instead of mean temperature anomalies (as tropospheric data better reflect daytime temperatures when there is deep convective overturning), I have replicated his findings.  Note that BOM maxima and rainfall are converted to anomalies from 1981 to 2010, the same as UAH.

Figure 2 is my plot of the running centred 13 month correlations between BOM maxima anomalies and UAH Australian region anomalies for all months of data from December 1978 to February 2018.

Figure 2:  Centred running 13 month correlation between BOM maxima and UAH:

BOM max v uah correl

There are some differences, but like Tom, I find a distinctly low, in fact, negative, correlation in the mid-nineties, centred on April 1996.

However, as I showed in my post “Why are surface and satellite temperatures different?”  in 2015, most of the difference between UAH and BOM maxima can be explained by rainfall variation alone.

Figure 3 is a plot of the monthly difference between UAH and BOM data plotted against rainfall anomalies (also calculated from 1981-2010 means).

Figure 3:

Diff v rain plot

R-squared of 0.54 means a correlation coefficient of 0.73.

This is how the correlation varies over time:

Figure 4:

Diff v rain correl

I have a problem.

There is a major drop in July 1995, but other big ones- October 1998, July 2003, December 2009, September 2015, and the most recent figure, August 2017.   Correlations are much more variable from 1995.  What can be the reason for these poor correlations?

There is also a general decrease in correlation over the years since 1978.

What’s wrong?  Surely rain gauges can’t be faulty?

Has there been a drift in accuracy of the UAH data?

Or has there been a drift in accuracy of BOM temperature measurement?

Any suggestions would be most welcome.

Post Script:

The major drops may occur at about the same time as major ENSO changes, though not always.  This graph plots the above correlations and 13 month centred averages of the SOI (scaled down) together.

Figure 5:

SOI and correlations

The SOI has not been lagged in this plot.  Perhaps the major changes in trade winds, monsoons, and the sub-tropical ridge affect tropospheric temperatures differently from surface temperatures at these times.  But that doesn’t explain the gradual decrease over time.

 

 

Pretty Patterns

March 13, 2018

Most people like pretty patterns.  They are pleasing to the eye.  But that’s no reason to create them when homogenising data, as the Bureau of Meteorology does when creating its ACORN-SAT datasets for a number of sites.

I am indebted to Bob Fernley-Jones, who noticed this and has been trying without success to point out to the Bureau that they need to address this issue.

For example, the Bureau found problems with maximum data from Darwin, especially before the Post Office and its thermometer were blown to bits by a Japanese bomb in February 1942.  Adjustments were needed as the data source moved from the town to the RAAF base.  Before this, apparently the Stevenson screen had become partially shaded by vegetation.  The problem is that the only other stations available for comparison for identifying and adjusting for discontinuities in the data were hundreds of kilometres away- Port Keats Police Station is 243 km away, Katherine is 270 km away, and Wyndham Port is 446 km away.  Port Keats and Katherine have monthly data from 1938 and 1937 respectively (but with many months of data missing from Katherine), and Wyndham Port has daily data available for the whole 1910-1942 period.  So these three distant sites were used to adjust Darwin’s raw data before 02/02/1941, but only Wyndham Port was used to make adjustments for all data before 01/01/1937 and 01/01/1916.

Here is the result.

Figure 1:  Adjustments to Darwin’s daily maxima 1910 to 1942

Darwin daily adj 1910 1942

Now isn’t that a very pretty and pleasing pattern?  The red line shows the difference between Darwin Acorn Tmax and Darwin raw Tmax, for every day from 01/01/1910 to 31/01/1942, revealing a repeating oscillation in values.  Note that from 2 February 1941 there are no adjustments.

The next plots analyse the three distinct periods by month of the year.

Figure 2:  Daily adjustments to Darwin’s maxima 01/01/1937 to 31/01/1941

Darwin daily adj 1937 to 41 max

Note that these are not mean values:  every single day in each month was adjusted by exactly the same amount as every other day in that month.  Every day in June 1937 was cooled by -0.5 degrees C, and likewise every day in June 1938, 1939, and 1940.  Days in April and December were not adjusted, while the Wet months were warmed and the Dry and Build-up months were cooled.  So much for the Bureau’s explanation that only Winter (-0.47) and Spring (-0.57) were adjusted.

Figure 3:  Daily adjustments to Darwin’s maxima 01/01/1916 to 31/12/1936

Darwin daily adj 1916 to 36 max

Again, every single day in each month has been adjusted by exactly the same amount as every other day in that month.  Days in the Wet were cooled by from -0.2C to -1.2C, while days in the Dry and Build-up months were cooled by -1.2C to -2.2C.  That’s some pretty savage adjusting, and does not vary from the first to the last day of each month.

Figure 4:  Daily adjustments to Darwin’s maxima 01/01/1910 to 31/12/1915

Darwin daily adj 1910 to 15 max

Note again that while the adjustments are not as large as 1916-1936, only February has no adjustment to raw data, and all other months have daily cooling adjustments which are the same from the start to the finish of the month.

Unbelievable.

Time for a clean out.

 

 

 

Fingerprints of Greenhouse Warming: Poles Apart

February 26, 2018

If global warming is driven by the influence of carbon dioxide and other man made greenhouse gases, it will have certain characteristics, as explained by Karl Braganza in his article for The Conversation (14 June 2011).

As water vapour is a very strong greenhouse gas, it will tend to mask the influence of man made greenhouse gases, and because solar radiation is such a powerful driver of temperature, this also must be taken into account.  Therefore, the characteristic greenhouse warming fingerprints are best seen where solar and water vapour influences can be minimised: that is, at night time, in winter, and near the poles.  So we would look for minimum temperatures rising faster than maxima; winter temperatures rising faster than summer, and polar temperatures rising faster than the tropics.  Indeed, polar temperature change in winter should be an ideal metric, as in Arctic and Antarctic regions the sun is almost completely absent in winter, and the intense cold means the atmosphere contains very little water vapour.  We can kill three birds with one stone, as winter months in polar regions are almost continuously night.

So let’s look at the evidence for greater winter and polar warming.

Figure 1: North Polar Summers:

NP summers

Figure 2:  North Polar Winters:

arctic all winters

Yep, North Polar winters are warming very strongly, at +2.58C/100 years, and much faster than summers (+1.83C/100 years)- strong evidence for anthropogenic global warming.  And warming is much faster than the Tropics (+1.023C/100 years):

Figure 3: Tropics

Tropics TLT

Unfortunately for the theory, the opposite happens in the South Polar region:

Figure 4: South Polar Summers

SP summers

Figure 5:  South Polar Winters:

antarctic all winters

While summers are warming (+0.58C/100 years), winters are cooling strongly at -1.66C/100 years.  Over land areas, with little influence from the ocean, very low moisture, and very little solar warming, winters are cooling even faster:

Figure 6:  Antarctic winters over land:

antarctic land winters

This is the exact opposite of what is supposed to happen in very dry, cold, and dark conditions- at night, in winter, at the poles.  Can this be because carbon dioxide and other greenhouse gases are NOT well mixed, and are in fact decreasing in concentration near the South Pole?

Figure 7: Carbon Dioxide concentration at Cape Grim (Tasmania):

C Grim CO2

Figure 8:  South Polar region TLT (all months) as a function of CO2 concentration:SP vs co2

No, while Cape Grim data show CO2 concentration to be increasing in the Southern Hemisphere, but without the marked seasonal fluctuations of the Northern Hemisphere, there is NO relationship between CO2 and temperature in the South Polar region.

Is it because the oceans around Antarctica are cooling?

Figure 9: South Polar Ocean TLT:

SP ocean

Nope- -0.01C/100 years (+/- 0.1C).  Neither cooling nor warming.

The cold, dry, dark skies over Antarctica are getting colder in winter.  Summers show a small warming trend.

Conclusion:  The fingerprints of man made greenhouse warming are completely absent from the South Pole, and differences between North and South Polar regions must, until shown otherwise, be due to natural factors.

Data sources:

https://www.nsstc.uah.edu/data/msu/v6.0/tlt/uahncdc_lt_6.0.txt

http://www.csiro.au/en/Research/OandA/Areas/Assessing-our-climate/Latest-greenhouse-gas-data

Mandated disclaimer:-

“Any use of the Content must acknowledge the source of the Information as CSIRO Oceans & Atmosphere and the Australian Bureau of Meteorology (Cape Grim Baseline Air Pollution Station) and include a statement that CSIRO and the Australian Bureau of Meteorology give no warranty regarding the accuracy, completeness, currency or suitability for any particular purpose and accept no liability in respect of data.”

BEST Adjustments

February 11, 2018

Two years ago I wrote a post about changes in Diurnal Temperature Range (DTR) and whether these were a “Fingerprint of enhanced greenhouse warming”, as claimed by Dr Karl Braganza in an opinion piece at The Conversation in 2011, and in his 2004 paper.

It being time to check more recent data (in 2016 the BEST data finished at December 2015), I went to the BEST site and downloaded the most recent monthly data for maxima and minima, which now extends to July 2017.

I should not have been surprised to find that the two datasets, produced 18 months apart, are different.  The differences are not large enough to be immediately apparent (from 1850 to 2015 the increase in trend per 100 years is only 0.023 degrees Celsius for maxima and 0.007C for minima), but they are none-the-less influential.

Here’s why.

Fig. 1: BEST Tmax 2016 minus 2017 (above zero means the data has been cooled, below zero means it has been warmed.)

BEST max diff

Note the large corrections before 1910, but the overall effect is minor.

Fig. 2:  BEST Tmin 2016 minus 2017

BEST min diff

I have shown the zero value, meaning no adjustment.  Note the large adjustments pre-1910 (but at different times to maxima); apart from two short periods, the whole series is WARMED by about 0.1C; I have marked with arrows the period from the late 1950s to the early 1980s when adjustments were minimal; but note the sudden drop (from January 1983) with recent minima WARMED by about 0.1C.

They have warmed the present and pre-1950, but left the cool 1950 – 1980 period largely alone.   What effect would this have?

Not much if you are looking only at temperature- they certainly can’t be accused of the more usual cooling the past and warming the present.  But if you are looking to find fingerprints of greenhouse warming, this is gold.  One of the fingerprints of enhanced greenhouse warming is greater warming at night than during the day, such that the Diurnal Temperature Range decreases.

The effect is subtle.  There is virtually no change in the long term DTR trend from 1850.

Fig. 3:  Diurnal Temperature Range calculated from BEST 2016:

BEST dtr 1850 2015

Fig. 4:  DTR calculated from BEST 2017:

BEST dtr 1850 2015 2017 version

But there is much uncertainty in data before 1910 as we are told, which is why BOM climate datasets start from 1910.

Fig. 5:  DTR 1910 – 2015 from BEST 2016:

BEST dtr 1910 2015 2016 version

Fig. 6:  DTR 1910 – 2015 from BEST 2017:

BEST dtr 1910 2015 2017 version

Again, virtually no change.  Aha, I hear Global Warming Enthusiasts chortle, gotcha!

The real effect of the adjustments is on the period from 1950, when man-made atmospheric carbon dioxide began increasing rapidly.

Fig. 7:  DTR 1950 – 2015 from BEST 2016:

BEST dtr 1950 2015 2016 version

Note the linear trend value: that equates to less than -0.1C per 100 years- a clear fault with the 2016 BEST data.  But with the new, improved 2017 version, the downward trend in DTR becomes:

Fig. 8:  DTR 1950 – 2015 from BEST 2017:

BEST dtr 1950 2015 2017 version

A three-fold increase in the downward trend in DTR.  This is much better support for the narrative of strong greenhouse warming since 1950.  How convenient.  We just have to wait for the papers and publicity about new evidence for decreasing DTR.

But Global Warming Enthusiasts wouldn’t want us to look at shorter time frames, particularly starting from the dog-leg which still exists from 1983, despite BEST’s warming of the minima data since then by about 0.1C.  This graph includes data to July 2017.

Fig. 9:  DTR 1983 – 2017

BEST dtr 1983 2017 2017 version

That looks like a rather long period of increasing DTR- not good evidence for the meme.  Don’t worry, they’ll explain that by claiming it’s due to “increased cloud and rain” since 1983, and besides, you have to look at the long term trend.

So be prepared for papers and press releases spruiking new confirmation that greenhouse warming is real, as evidenced by strong DTR decrease since 1950.

And all because of almost undetectable changes to the BEST datasets.

Summer Temperatures in South-Central Queensland Part 2: Weather Events and Spikes

October 30, 2017

In my last post I showed how on average temperature changed diurnally across a number of Queensland BOM stations.  In this post I will show examples of temperature change at some of these stations.  I am using “one minute data’, which despite its name, is really the value at the last second of every minute, in other words, sampling at 60 second intervals.

We know that temperatures spike up and down every few seconds, but these spikes are not captured by the Bureau unless they are the highest and lowest for each minute, and only noticed by a keen observer if the highest or lowest temperature spike so far that day occurs in the same minute (usually on the hour or half hour) as final second temperature reported at the Station Observations page.

Let us begin with this plot of a temperature spike at Maryborough Airport on 15 February, kindly reposted by Anthony Watts.  This was one of many examples from different locations around Australia of times when the maximum temperature of the day occurred in the same minute as a half-hourly recording, but exceeded it by a large amount (1.5 degrees in this example).

Figure 1:

Mboro 15 Feb

Please note that for this plot I only had access to the half hourly data from the Bureau, supplemented with some time offset data from the UK Met Office, usually 10 minutes before the BOM values.  With the higher resolution given by one minute data, we can gain a better appreciation of what was happening on this day.

Figure 2:

1 min T Mboro 15 feb

Note the spike at 13:00.  It is just part of the constant fluctuation during daylight hours which is not apparent from the data available for Figure 1.

Let’s have a closer look at the period from 12:00 to 14:00.

With the caveat that we can only guess at the 59 one second values in between the final second samples, we can use the latter values to investigate temperature response by day and night to various influences.  Assuming that the intervening one second fluctuations are approximately equally above and below a 60 second de facto mean represented by the value at the final second (as the Bureau’s Fast Facts would have us believe), a centred 5 minute mean of one minute (final second) data would approximate a mean of the complete 300 seconds.  I use a centred 5 minute mean to compare with the one minute data, but please understand this is an approximation, a best guess, when applied to short time lengths.  Its real value will be with all 115,200 data points- more later.

Figure 3:

1 min T Mboro 15 feb 12 to 2

Firstly, note how well the five minute centred mean represents most of the larger fluctuations, while considerably smoothing the final second data.

Secondly, note that the day’s maximum, 33.7C, was reached in the final second of 12:59, and was still at 33.7C at some second of the next minute, before falling 1.5 degrees to 32.2C in the final second of 13:00.

Thirdly, note that if this was a station in the USA, where 5 minute means are used, the maximum for the day would have been approximately 32.5C, still 1.2C less than the official value.

The temperature also fell 1.6C in the 60 seconds to 12:53.   And here are all the minute to minute temperature changes at Maryborough on 15 February (large outliers circled).

Figure 4:

1 min T change by hr of day Mboro 15 Feb

As shown in the previous post, this is the typical diurnal pattern.  Figure 5 shows one minute temperature fluctuation for the whole period, 1 January to 21 March 2017.

Figure 5:

1 min T change by hr of day Mboro 1 Jan 21 Mar 2017

Note the swelling of fluctuation in daylight hours, the constriction at sunset and sunrise as heating/ cooling regimes change, and the outliers: values can change by up to +2.3C or -2.1C in 60 seconds.

And here is an example of how a day’s temperature can change quite naturally, but we have to ask: would a mercury thermometer be able to match this?

Figure 6:

1 min T Mboro 6 mar

I now turn to other stations.  Hervey Bay Airport is about 30km from Maryborough Airport, only a couple of kilometres from the sea.  Firstly, how temperature changes from one minute to the next for the whole period.

Figure 7:

1 min T change by hr of day Hervey Bay 1 Jan 21 Mar 2017

Note that the daily increase in fluctuation is much less than at Maryborough.  Hervey Bay Airport is only a couple of kilometres from Sandy Strait, and proximity to a water body may be a tempering influence.

Note also the large outlier of -2C in one minute- still less than the 2.2C downwards spike on 22 February in less than a minute, which prompted my first query to the Bureau!  What could have caused such an outlier?  Here’s the one minute temperature plot for 16 March:

Figure 8:

1 min T Hervey Bay 16 March 2017

This outlier was the result of an entirely natural weather event, a sudden cool change, possibly a storm front: 4.4mm of rain was recorded at 09:00 on the 17th.  Would a mercury thermometer be sensitive enough to capture that?

And here’s 22 February:

Figure 9:

1 min T Hervey Bay 22 Feb 2017

Note the unusual spiking between about 04:30 and 06:30.  Something was going on.  Note also that the minimum temperature at 06:00 was far below at 23.2C, 1.6 degrees below any other temperature that day- for one second.

I now turn to Thangool Airport, a few kilometres from Biloela in the Callide Valley, 150km from the coast.

Figure 10:

1 min T change by hr of day Thangool 1 Jan 21 Mar 2017

Note the same shape, and though much further inland, not apparently different range from Maryborough.  Most of the change between 09:00 and 15:00 is within the bounds of +/- 1 degree each minute, but there are many outliers.

I shall now look at how temperature changed on a sample of days.  Firstly, 31 January shows a typical temperature curve for a clear sunny day.

Figure 11:

1 min T Thangool 31 jan

Figure 12 shows 7 January, a day with a mid-morning drop.  0.2mm of rain was recorded on the 8th.

Figure 12:

1 min T Thangool 7 jan

Note how after the sudden plunge the temperature quickly returns to “normal” as if nothing has happened.

28 January shows a late afternoon drop with a smaller recovery until sundown.

Figure 13:

1 min T Thangool 28 jan

Figure 14:

1 min T Thangool 24 jan

Note the typical warming curve which lasts until 16:47 when there is a sudden drop of 2.3 degrees in 3 minutes, with continued cooling.  I suspect a wind change was the cause.

Figure 15:

1 min T Thangool 20 mar

This shows a midday weather event, with the rapid return to the “normal” curve.  6mm was measured next morning.

Figure 16:

1 min T Thangool 17 feb

Note the sudden spike mid-morning.  The temperature spikes nearly 4 degrees in a few minutes to a value not expected for another hour or two.  This is odd and I cannot think of a natural weather event that could be the cause.  Whatever the cause, I doubt a mercury thermometer would track this change.

The final station for this post is at Lady Elliott Island, about 80km off the coast in the Coral Sea.  The screen is on white coral sand, about 100 metres from the water to the east.  First, one minute change over the whole period.

Figure 17:

1 min T change by hr of day L Elliott Is 1 Jan 21 Mar 2017

Note again the typical shape, but with much smaller daytime range of changes than inland sites.  Upward outliers are muted (there is only one instance of a temperature change in one minute of more than one degree).  However, downwards outliers are large and occur throughout the 24 hour period.

Here are some plots of several days on a tropic island.

Figure 18:

1 min T L Elliott Is 7 jan

Note the early morning downward spikes: rain showers.

Figure 19:

1 min T L Elliott Is 16 jan

Note the sudden drop just before midday: another rain shower.  But note how the temperature quickly returns to nearly what it was before.

Figure 20:

1 min T L Elliott Is 28 jan

Again, morning showers (quite normal near the sea in the wet season).

Now for the largest one minute temperature drop of -2.3 degrees just before midnight on 14 March.

Figure 21:

1 min T L Elliott Is 14 mar

Now watch the temperature recovery next day.

Figure 22:

1 min T L Elliott Is 14 15 mar

So, with a drop of nearly 6 degrees in a few minutes, this was a perfectly natural weather event.  Apart from sudden weather generated decreases like those shown above, it seems that there is a floor to minima of about 26C to 27C, due of course to the sea temperature.

While these examples are interesting, what about a day with sunny, fine weather?  Here’s the plot for 16 February.

Figure 23:

1 min T L Elliott Is 16 feb

Note a much more regular daytime curve (with rapid large spikes between 09:00 and 15:00), peaking only just after midday- except for a spike at about 14:30.  Here’s a closer look at the time from 12:00 to 15:00.

Figure 24:

1 min T L Elliott Is 16 feb 12 to 3

The second largest downwards spike (-1.3C) of the whole record occurred at 14:32.  This was purely a spike, not due to any weather event.  Could a mercury thermometer possibly match this?  If not, it would not reach the same maximum (30.8C).  On a hot sunny day on a coral island 100 metres from the sea, daytime temperature spikes up and down rapidly by up to a degree (or more) at a very high frequency.  Compare this with Maryborough in Figure 3.

This confirms generalisations I made in my last post:

“Temperatures in daylight hours are very volatile, while at night temperatures change very little except in unusual weather events.  Fastest and most sustained warming is in the hour after sunrise.  Fastest and most sustained cooling is also in daylight hours.  Night time cooling is much more gradual.  Cooling is on average more rapid than warming.  Rapid warming occurs when the sun suddenly appears.  Rapid cooling is associated with weather events such as rain storms.”

The Bureau of Meteorology have claimed that their AWS sensors are so designed that they mimic the mercury in glass thermometers they have replaced.   They claim a mercury in glass thermometer would track the above fluctuations closely.  However they have as yet provided no papers or comparative data to back this up.  From analysis of these stations’ data, I find that hard to believe.

Again we say, show us the data.

Summer Temperatures in South-Central Queensland Part 1: Diurnal Patterns of Temperature Change

October 15, 2017

In March of this year I purchased from the Bureau of Meteorology one-minute temperature data for the period 1 January to 21 March 2017, for a number of Queensland stations within 250km of Bundaberg.  “One-minute temperature data” is not the temperature of the whole minute, but means temperatures at  of the final second of each minute, so are spot samples taken at regular intervals.  Temperatures can be higher and lower in the intervening seconds, and so for example daily maxima can be several tenths of a degree or more above the final second values, as I demonstrated in earlier posts.

I have analysed data from these stations:  Maryborough, Hervey Bay Airport, Gayndah Airport, Thangool Airport, Bundaberg Airport, Rosslyn Bay, Gladstone Radar, Gladstone Airport, Rundle Island, Nambour, Kingaroy, Tewantin, Maroochydore, Gympie, Double Island Point Lighthouse, and Lady Elliott Island.  Most of these have few missing observations, but all still needed tedious checking.  Kingaroy’s record is atrocious, with days and weeks of intermittent data drop out.

I looked at: one minute temperature change, that is, from one data point to the next; temperature change after 10 minutes; the number of minutes of uninterrupted rise; the number of minutes of uninterrupted fall; and the number of minutes the temperature remained at the same value.

In this post I firstly plot averages of the above metrics across all 16 stations by time of day, to show the range of temperature variation from one minute to the next throughout the day and night, in distinctive diurnal patterns.

Figure 1:  One minute temperature change:-

Mean 1 minute dT

All stations show this distinctive shape, with some variance in range from island to inland stations.

Remember, this plot shows the average of 16 stations every minute of every day for 80 days.

Note the narrow range (averaging less than +/-0.1C) between sunset and sunrise, and the much larger swings from one minute to the next in daylight hours, especially between 09:00 and 15:00.  Outlier points are from weather events at individual stations.

The next plot shows the range of temperature change over 10 minute periods:

Figure 2:  10 minute temperature change:-

Mean 10 minute dT

Note the sharp increase from shortly after sunrise to an early morning peak, then a gradual decrease in the mean to a small dip at around 6 p.m..  Note again the small variation in the absence of the sun, and the many individual weather events shown by outliers.

The next plot counts the number of minutes when the temperature increases each minute at least +0.1C.

Figure 3:  Uninterrupted temperature increase:-

Mean Duration Rising

As you might expect, temperatures rise predominantly during daylight hours, with a sudden jump up just after sunrise, and a dip at sunset.

The next plot counts the number of minutes when the temperature decreases each minute at least -0.1C.

Figure 4:  Uninterrupted temperature decrease:-

Mean Duration Falling

Temperatures generally don’t fall very much just after sunrise.  However note that between 0900 and 1800 it is very rare for the temperature to be falling for zero minutes.  Most long temperature falls occur in daylight hours.  Surprising? What goes up must come down.

The next plot shows the length of time when the temperature does not change from one minute to the next:

Figure 5:  Unchanged temperature:-

Mean Duration Unchanged

Note that during the night on average temperatures are never the same for zero minutes (i.e. they are frequently the same), while in daylight hours temperatures are much less stationary, with a gradual rise from 1500.

The next graphs show the range of these metrics for individual stations.  This will be explored further in a future post.

Figure 6:  One minute temperature change:-

Max min dT comp

This shows the fastest minute to minute temperature change, both up and down.

Figure 7:  10 minute temperature change:-

Max min dT10 comp

Note that there was much faster cooling than warming over 10 minute periods, mostly associated with rain showers, storms, or cool changes.

Figure 8:  Uninterrupted temperature increase:-

Max Duration Rising comp

Figure 9:  Uninterrupted temperature decrease:-

Max Duration Falling comp

Note that Lady Elliott Island (far out to sea) and Rundle Island (in Gladstone Harbour) both had shorter periods of constantly rising and falling temperature.

Figure 10:  Unchanged temperature:-

Max Duration Unchanged comp

On the night of the 6th March at Maroochydore Airport the temperature was 26.1 degrees for 118 minutes.  As you can see nearly all stations had stable temperatures for nearly an hour on at least one occasion.

These results confirm that temperatures in daylight hours are very volatile, while at night temperatures change very little except in unusual weather events.  Fastest and most sustained warming is in the hour after sunrise.  Fastest and most sustained cooling is also in daylight hours.  Night time cooling is much more gradual.  Cooling is on average more rapid than warming.  Rapid warming occurs when the sun suddenly appears.  Rapid cooling is associated with weather events such as rain storms.

In Part 2 (probably not for a week or two) I will look at daily warming and cooling at individual stations.

Replicating Lewis et. al. (2017): Another Junk Paper

October 9, 2017

The recently released scarey predictions about “50 degree temperatures for Sydney and Melbourne” touted by Sophie Lewis are hardly worth wasting time on.  The paper is

Australia’s unprecedented future temperature extremes under Paris limits to warming, Sophie C. Lewis , Andrew D. King  and Daniel M. Mitchel, (no publication details available).

The paper is junk.  It has some very sciencey sounding words but is at heart pure speculation.  Like most “projections” by Global Warming Enthusiasts, the predictions are untestable.  Scarey temperatures are possible IF (and only if) IPCC scenarios are valid and we get either 1.5C or 2C warming by the last decade of the century.  That’s what the paper rests on.

The paper looks at Australian summer means, Coral Sea autumn means, and New South Wales and Victorian daily January maxima.  AWAP data are used for Australia and NSW and Victoria, and HadCruT4 for the Coral Sea region (which includes most of Queensland).

I have just looked at Australian Summer Means, and that was enough for me.  Lewis et.al. say that the decadal mean from 2091-2100 may have Australia wide summer means of 2 to 2.4 degrees above the mean of 2012-13, or 30.1 to 30.5C, with resultant very high daily maxima in southern cities.

I could have saved them the trouble, and at considerably less cost.

All I needed was the AWAP data for summer means (I purchased monthly AWAP data up to 2013 a couple of years ago), and plotted it with a 2nd order polynomial (quadratic) trend line:

lewis predictions summers1

And also showing decadal means (although the first and last decades have several missing summers):

lewis predictions summers2

There: the trend line goes smack through the higher (+2 degrees) projection, so it must be right!

Only trouble is, extrapolating with a quadratic trend is not a good idea. Lots can go wrong in the meantime.

So my plot is about as useful as the Lewis et.al. paper, and that’s not much.

Australian Temperature Data Are Garbage

September 14, 2017

From the Bureau’s hastily published “Fast Facts”:

“This means that each one second temperature value is not an instantaneous measurement of the air temperature but an average of the previous 40 to 80 seconds.”

That is complete nonsense.

At the end of each minute, the following data are recorded:

  1. Lowest one second reading of the previous 60 seconds
  2. Highest one second reading of the previous 60 seconds
  3. Reading at the final second of the minute.

Firstly, 40 seconds is not one minute, the integration period recommended by the WMO in 2014 and by the Bureau’s own officers in 1997.  Anything less than 60 seconds is not compliant.

Secondly, consider this plot, which is from actual 1 minute temperatures recorded at Hervey Bay Airport on 22 February 2017.  (Data purchased by me from the Bureau).

Fig. 1:

Hervey Bay 1 min 5 to7am 22 Feb

Sunrise was at about 5:40 a.m.  Temperatures do not increase until about 6:30 a.m.  Note the strangely low temperature- the daily minimum- which was reported as occurring sometime in the 60 seconds before 06:00:00.  The BOM would have us believe that each of the values in Figure 1, including the low of 23.2C, are “averages” of the previous 40 to 80 seconds.

Next consider what happens in that minute from 5:59 to 6:00, as per the following plot.

Fig. 2:

Hervey Bay 1 min 0559 to 0600am 22 Feb

We don’t know in which seconds the high and low readings for that minute occurred, so I have shown them for each of 59 seconds.  I have shown the 5:59 and 6:00 readings: both were 25.3C.

Consider how the value at 06:00 was obtained:

If by an “average” (however derived) of less than 60 seconds, the methodology is non-compliant.

If by an “average” of the previous 60 seconds, it must include values that contributed to the High of 25.4C and the Low of 23.2C.

If by an “average” of anything greater than 60 seconds, it must include values that contributed to both the Low and High values, and as well, values that contributed to the 5:59 reading- which is the same as the 06:00 reading.

Similar logic applies to the Low and High readings.

It follows that the intermediate instantaneous atmospheric temperatures that contributed to all three reported “average” values must have ranged from much higher than 25.4C to very much lower than 23.2C.

Look at Figure 1 again.  The air temperature at Hervey Bay on 22 February must have spiked down very much lower than the 23.2C plotted.

Really?

In the early morning there is very little near ground turbulence so temperatures do not fluctuate from one minute to the next by very much.  In How Temperature Is Measured in Australia Part 2 I showed that 91% of low temperatures vary from final second temperatures in the same minute by 0.2C or less.  A difference of 2.1C is extraordinary.  Fluctuations greater than that are difficult to believe.

However, in a comment at How Temperature Is Measured In Australia Part 1, Tony Banton, a retired meteorologist, says that the BOM explanation of cooler ground level air mixing upwards is correct.  If we accept that explanation, we must then face the problem of “comparability”.

In 61 seconds, the Hervey Bay AWS has reported temperatures of 25.3, 25.4, 23.2, and finally 25.3 degrees.  The BOM asserts that a liquid-in-glass thermometer will be able to respond as quickly and show similar temperatures- and remember, 23.2C was the morning’s official minimum.

My response: rubbish.  The data for 22 February at Hervey Bay show that no averaging is used at all, and the Low Temperature of 22.3C  23.2C is an instantaneous one second recording from a rogue downwards spike, whatever the cause, whether a natural event or other (e.g. electrical) factor.

Temperatures reported by the BOM are not fit for purpose of accurate reporting of maxima and minima, identifying records, or identifying warming or cooling by comparison with historic liquid-in-glass data.