Posts Tagged ‘temperature’

ACORN-SAT 2: Eucla: The Devil in the detail

February 18, 2019

I’m having a break from looking at Acorn 2 data from Queensland.  I’ve been wondering:  what’s going on?  What’s beneath these changes?  In particular, I was struck by statements in the accompanying Research Paper that

In total, there were 966 adjustments applied in version 2 of the ACORN-SAT dataset, 463 for maximum temperature and 503 for minimum temperature.”

The Bureau is referring to breakpoints in the data where adjustments are applied to all previous years.  In the daily data, there are tens of thousands of adjustments at each station.

For example, in Eucla’s Tmax record, there are 34,145 daily datapoints; 34,144 in Acorn 1; and 33,858 in Acorn 2.  There are  10,190 instances where Acorn 1 makes no change to raw data, and 9,312 in Acorn 2.  Most of the instances of no adjustments are since 1995.  Before then almost every day has been adjusted.

And the devil is in the detail.

The following plots show how adjustments are applied to the range of raw maxima.  First Acorn 1.

Figure 1:  Acorn 1 adjustments as applied to raw maxima at Eucla

Ac1 raw adj

Figure 2:  Acorn 2 adjustments as applied to raw maxima

Ac2 raw adj

Acorn 2 removes the large negative adjustments for temperatures in the high 30s, and the spread is wider for very high temperatures.  So far so good.

Figure 3 shows where many of these adjustments are made.

Figure 3:  Acorn 2 and  raw maxima

Eucla 1913-2017

Between 1930 and 1995 many high temperature spikes are reduced by 5 degrees and more.

For example, here is November 1960.

Figure 4:  Raw, Acorn 1, and Acorn 2 in November 1960

Eucla Nov 1960

The Bureau can truthfully claim that there is a balance between positive and negative adjustments.

However, note how all temperatures over 35C have been reduced by five degrees.  This is common across these years.

Perhaps temperatures on very hot days at Eucla in the 1960s were exaggerated?  Perhaps they were not read accurately?

If this pattern of hot day reductions is generally followed at stations across large regions, e.g. southern Australia, the effect will be that climate analysis based on Acorn 2 will show that past extremes were generally not as high as nowadays.

And that can’t be a bad thing for the meme.

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ACORN-SAT 2.0: The Northern Territory- Alice in Wonderland

February 15, 2019

(UPDATE 17/02/2019:

I have corrected a glitch in trend calculations which are now as shown.  I have deleted all Diurnal Temperature Range plots and discussion as well.)

This is the second in a series of posts in which I directly compare the most recent version of Australia’s temperature record, ACORN-SAT 2, with that of the previous version, ACORN-SAT 1.  Daily data are directly downloaded from the Bureau of Meteorology. I do not analyse against raw data (available at Climate Data Online), except for particular examples, as I am interested in how different Acorn 2 is from Acorn 1.  The basis for the new version is in the Research Report.

See my previous post for Western Australia for a general introduction.

The Context – The Northern Territory

Figure 1 is a map of Australia showing all of the Bureau’s ACORN-SAT climate monitoring stations.  The Northern Territory is right in the Outback, from the monsoonal north to the desert centre. Most of it is savannah or desert, and there are vast distances between settlements and thermometers.

Figure 1:  Australian ACORN-SAT stations

map NT

There are five Acorn stations in the Northern Territory BOM database.  Differences between Acorn 1 and Acorn 2 are summarized in the following sections.

Trend changes

Trends in maximum temperature have changed a lot at individual stations, but on average there has been little change  (+1.29C to +1.27C per 100 years).  (Even though an average of such wildly different stations across such vast territory is meaningless.)

Figure 2:  Maxima trend changes from Acorn 1 to Acorn 2

NT max trend

The “average” change in minima is -33.3%  (+0.55C to +0.37C per 100 years).    This however is mainly due to Rabbit Flat’s short history with much missing data.

Figure 3:  Minima trend changes from Acorn 1 to Acorn 2

NT min trend

Largest temperature differences

In maxima, changes to Acorn 1 daily data were mostly small, except at Alice Springs which had adjustments ranging from -9.2C to +10.1C applied to individual daily figures, but only on a few days.  The +10.1C adjustment was to correct what could only have been a typographical error in Acorn 1, which recorded 26.8C instead of 36.8C on 28 January 1944.  The -9.2C is less easily explained and may be the opposite, Acorn 2 recording 24.1C instead perhaps of 34.1C on 6 March 1943.  Acorn 2 made many other large corrections around these dates, as Figure 4 shows.

Figure 4:  Daily changes in maxima from Acorn 1 to Acorn 2 at Alice Springs

max diff alice

Minima adjustments ranged from -11.5C to +11C also at Alice, and there were many other large adjustments as well.  At the other stations the range was much less, though still substantial changes (-3.6C to +4.6C) to Acorn 1.  Here is Alice Springs again:

Figure 5:  Daily changes in minima from Acorn 1 to Acorn 2 at Alice Springs

min diff alice

(Remember, these are adjustments to Acorn 1, which was supposed to be “world’s best practice” seven years ago.  How did Blair Trewin get it so wrong the first time?  Has world’s best practice changed so much in seven years?)

Record temperatures

A new record maximum was established at Darwin, whose record on 18 October 1982 (unchanged from raw to Acorn 1) increased from 38.9C to 39.5C in Acorn 2.

Figure 6:  Three versions of maxima at Darwin 18 October 1982

Darwin max 1982

A slightly higher record was also set at Victoria River Downs.

A new record low temperature on 21 June 1925 was also established at Alice Springs, where the Acorn 1 temperature of -6.7C was reduced to -9.4C.   (The temperature in the Post Office raw data was -5.6C.)  New lows were established at Darwin and Tennant Creek as well, but on nothing like the same scale.

Apparently the adjustments made to raw data in Acorn 1 weren’t big enough.

Quality Control: especially minimum temperatures higher than maximum.

In Acorn 1, 3 out of the 5 stations had at least one example of minimum higher than maximum.  Blair Trewin claims he has “fixed” this problem (which he concedes was “physically unrealistic”) by adjusting temperatures in Acorn 2 so that the maximum and minimum are the same, so that DTR for the day is zero.  In his words:

A procedure was therefore adopted under which, if a day had a negative diurnal range in the adjusted data, the maximum and minimum temperatures were each corrected to the mean of the original adjusted maximum and adjusted minimum, creating no change in the daily mean.

But that is not how he “corrected” the worst NT examples in Acorn 1 (minimum 4.8C above maximum at Alice Springs, and a 3.9C difference at Tennant Creek).  Here is a plot of the raw data and changes made by Acorn 1 and Acorn 2 at Alice Springs for 11 to 21 June 1932.

Figure 7:  Alice Springs Post Office data for 11-21 June 1932

Alice june 32 min2

Acorn 1 made no change to raw maxima, but was supposed to cool raw minima (the purple line) substantially  (the blue line).  Unfortunately, it is likely that instead of 8.1C, 18.1C was entered, human error resulting in garbage.  Acorn 2 has fixed this, but not by making minima and maxima equal to the Acorn 1 mean (15.7C), and neither is the DTR zero.  Instead there were more arbitrary adjustments.

(At Tennant Creek, to correct negative DTR of -3.9C,  minimum and maximum were both set to 22.9C, which is one degree less than the Acorn 1 mean of 23.9C).

 “Square wave” pattern in adjustments

The peculiar repeating pattern of adjustments to Perth in Acorn 1 also occurs at Darwin, but the pattern is even more bizarre.

Figure 8:  Darwin Acorn 1 daily maxima differences (pre-World War 2)

sq wave Darwin acorn 1

In every month, every day of the month was adjusted in Acorn 1 by exactly the same amount, which is the reason only 1917 is visible- the others are exactly the same.  Blair Trewin has taken notice of the criticism, and adjusted Acorn 2 with a little more intelligence, but the monthly pattern is still visible.  Adjustments are still applied month by month, especially in the Dry months.

Figure 9:  Darwin Acorn 2 daily maxima differences 

sq wave Darwin acorn 2

Conclusion:

There are no additional stations, so the network is still extremely sparse.

There is a very small amount of additional digitized data.

The average trend in maxima for NT has not changed very much, even though there is a large range across individual stations.  There was a reduction in the minima trend of -33.3%, mainly from the large impact of Rabbit Flat’s poor data.

Alice Springs had large differences between Acorn 1 and Acorn 2 daily data of over 11 degrees Celsius.

New record maximum and minimum temperatures have been set.

The issue of instances of minima being higher than maxima caused by too vigorous adjustments or human error has been “fixed” by arbitrary adjustments, and not as described in the research paper.

The bizarre “square wave” pattern in adjustments in Darwin has been largely rectified, at least in the Wet months.

With only five Acorn stations in the Territory, each one has a large impact on the climate record.  Alice Springs, which is said to contribute 7 to 10 percent of the national climate signal, has had extremely large adjustments made to Acorn 1.  VRD and Rabbit Flat, stations with short histories and incomplete data, also have a large impact on the national climate signal.

The size of the adjustments (made by comparison with stations up to 1,300 km away) only seven years after the “world’s best practice” dataset was launched, is incredible, and demands explanation.

Otherwise, it would appear that the temperature record of the Northern Territory, especially at The Alice,but also at other stations, has fallen down a rabbit hole, and appears to be out of a chapter from Alice in Wonderland.

Next: Queensland.

 

ACORN-SAT 2.0: Western Australia- A State of Confusion

February 14, 2019

(UPDATE 17/02/2019:

I have corrected a glitch in trend calculations which are now as shown.  I have deleted all Diurnal Temperature Range plots and discussion as well.)

This is the first in a series of posts in which I directly compare the most recent version of Australia’s temperature record, ACORN-SAT 2, with that of the previous version, ACORN-SAT 1.  Daily data are directly downloaded from the Bureau of Meteorology. I do not analyse against raw data (available at Climate Data Online), except for particular examples, as I am interested in how different Acorn 2 is from Acorn 1.  The basis for the new version is in the Research Report.

I start with Western Australia, and must thank Chris Gillham for his outstanding work and for allowing me to use data from stations he has used for his annual analysis.

Introduction:

The Bureau of Meteorology has released its latest revision of the Australian temperature record back to 1910.  Previous versions of our historic temperatures included “High Quality”, which I revealed in 2010 to have major flaws, not least being the strong warming bias; and ACORN-SAT 1, released in March 2012, proudly touted as being “World’s Best Practice”, which I (along with others) found to have very many severe problems.  (If you like, check these posts, here, here, here, and here.  There are many others.)

Stung by the public and media criticism which this generated, the Bureau set up a supposedly independent Technical Advisory Forum, which met on one day per year for three years and basically rubber-stamped Acorn.  They did, however, make some recommendations, particularly about transparency.  In the light of this recommendation, this latest release without any publicity at all is perplexing.

Nearly all of Australia’s climate analysis and modelling is based on the previous version, Acorn 1, including monthly, seasonal, and annual means, extremes, and trends.  Sometime in the near future, this will be based on Acorn 2 data.

As this an upgrade to an existing dataset, we might expect there would be a few small tweaks of maybe a few tenths of a degree in some records and any changes to temperature trends would be fairly small.  Perhaps there might be some extra stations in remote areas to improve the density of the sparse network, perhaps some records starting earlier because of newly digitized data, hopefully a sensible fix for the dreadful situation of many daily minimum temperatures being higher than the maximum.

Not so.

No wonder the Bureau has released Acorn 2 so quietly- it is a confusing mess, and completely alters Acorn 1.  Trends are vastly different, some temperatures altered by more than 10 degrees Celsius, and new records established.

The Context – Western Australia

Figure 1 is a map of Australia showing all of the Bureau’s ACORN-SAT climate monitoring stations.  Western Australia occupies the western third of the continent.  Most of it is desert, and there are vast distances between settlements and thermometers.

Figure 1:  Australian ACORN-SAT stations

Acorn map WA

There are 25 Acorn stations in the Western Australian BOM database.  One (Kalumburu 001019) has the latest version data for minima but not for maxima, so complete analysis is not possible.  Differences between Acorn 1 and Acorn 2 are summarized in the following sections.

Trend changes

Trends in maximum temperature have increased by an average of +0.25 degrees Celsius per 100 years (from +1.17C to 1.42C), which is an increase of 21.7% over the trend produced by Acorn 1.  (Click on each graphic to enlarge.)

Figure 2:  Maxima trend changes from Acorn 1 to Acorn 2

WA Max trend chart

The largest increase in trend is at Wittenoom.

Trends in minimum temperature have increased by an average of nearly +0.22 degrees Celsius per 100 years (from +1.04C to +1.27C), which is an increase of 21.53%.

Figure 3:  Minima trend changes from Acorn 1 to Acorn 2

WA Min trend chart

The largest increase  (+1.06C per 100 years- from +0.55C to +1.61C).  The largest decrease in trend was at Halls Creek: -1.31C per 100 years.

Largest temperature differences

In maxima, changes to Acorn 1 daily data were often very large.  Wandering gets the gong for greatest adjustments, ranging from -10.9C to +10.9C applied to individual daily figures, but only on a few days.  Eucla has many large changes made to Acorn 1 data.

Figure 4:  Daily changes in maxima from Acorn 1 to Acorn 2 at Eucla

Diff Tmax Eucla

Minima adjustments ranged from -10.8C at Esperance to +7.8C at Halls Creek for a few adjustments, but at most stations the range was much less, though still substantial changes to Acorn 1.  Here is Perth:

Figure 5:  Daily changes in minima from Acorn 1 to Acorn 2 at Perth

Diff Tmin Perth

(Remember, these are adjustments to Acorn 1, which was supposed to be “world’s best practice” seven years ago.  How did Blair Trewin get it so wrong the first time?  Has world’s best practice changed so much in seven years?)

Record temperatures

A new record maximum was established at Carnarvon, whose already homogenized record increased from 48.5C to 51C.  This is now the record for all of Australia, apparently (although I have 87 more stations to check).   Additional large adjustments are the cause:

Figure 6:  Three versions of maxima at Carnarvon 23 January 1953

Carnarvon Max

The previous “record”, held by Albany in the cool south, after much ridicule was reduced from 51.2C to 49.5C.  New records were also established at Bridgetown, Dalwallinu, Eucla, Kalgoorlie, Katanning, Marble Bar, Merredin, Perth, and Port Hedland.

New record low temperatures were established at Bridgetown, Cape Leeuwin, Cunderdin, Dalwallinu, Esperance, Eucla, Forrest, Geraldton, Halls Creek, Kalgoorlie, Learmonth, Marble Bar, Meekatharra, Perth, and Wittenoom.

Apparently the adjustments made to raw data in Acorn 1 weren’t good enough.

Quality Control: especially minimum temperatures higher than maximum.

In Acorn 1, 16 out of 25 stations had at least one example of minimum higher than maximum.  Blair Trewin has “fixed” this problem (which he concedes was “physically unrealistic”) by adjusting temperatures in Acorn 2 so that the maximum and minimum are the same, so that DTR for the day is zero.  In his words:

A procedure was therefore adopted under which, if a day had a negative diurnal range in the adjusted data, the maximum and minimum temperatures were each corrected to the mean of the original adjusted maximum and adjusted minimum, creating no change in the daily mean.

But that is not how he “corrected” the worst Western Australian example in Acorn 1 (minimum 2.1C above maximum) at Kalgoorlie.  Here is a plot of the raw data for 14th to 18th November 1914.

Figure 7:  Kalgoorlie Post Office data for 14-18 November 1914

Kalgoorlie raw

The 16th was a cold rainy day, with only 0.1C separating minimum (15.5C) and maximum (15.6C).  But temperatures in 1914 were read from a Fahrenheit thermometer.  Both 60F and 60.1F convert to 15.6C; 15.5C is 59.9F.  It is likely the temperature ranged from just under 60F to just over 60F.

Acorn 1 adjustments were made with brute force rather than finesse.  The maximum was reduced by 1.3C to 14.3C, and the minimum was raised by 0.9C to 16.4C, resulting in nonsense.

Figure 8:  Kalgoorlie Post Office and Acorn 1 data for 14-18 November 1914

Kalgoorlie Ac1

In Fahrenheit, 57.7F maximum and 61.5F minimum.

The solution in Acorn 2?  Even more brutal adjustments- and not to the mean of the Acorn 1 adjustments (which would have been 15.35C):

Figure 9:  Kalgoorlie Post Office and Acorn 2 data for 14-18 November 1914

Kalgoorlie Ac2

The Acorn 1 minima is decreased (by 3.4C) to 13C, and Acorn 1 maxima decreased by another 1.3C to 13C (or 55.4F), making it 2.6C below the raw temperature as read in 1914.  Now there is no problem with minimum exceeding maximum, but at the cost of raw data tortured beyond recognition.

“Square wave” pattern in adjustments

Bob Fernley-Jones first noticed a peculiar repeating pattern of adjustments to Perth in Acorn 1 monthly data.  I can replicate this in dailies.

Figure 10:  Perth Acorn 1 daily maxima differences 1983-1986

sq wave perth acorn 1

This pattern is still visible in Acorn 2, but is much reduced.  Adjustments are still applied month by month, but they are not as rigid.

Figure 11:  Perth Acorn 2 daily maxima differences 1983-1986

sq wave perth acorn 2

This is how it was changed:

Figure 12:  Perth Acorn 2 minus Acorn 1 daily maxima differences 1983-1986

sq wave perth acorn 2- acorn1

A new square wave- almost a mirror image of Figure 11.  It is good to see that the Bureau has taken notice of criticisms!

Conclusion:

Comparison of Acorn2 versus Acorn 1 data for Western Australia does not encourage confidence in the Bureau’s methods:-

There are no additional stations, so the network is still extremely sparse.

There is a very small amount of additional digitized data.

The average trend in maxima for WA has been increased by 21.7%, and in minima by 21.5%.

Differences between Acorn 1 and Acorn 2 daily data can be up to nearly 11 degrees Celsius.

New record maximum temperatures have been set.

The issue of instances of minima being higher than maxima caused by too vigorous adjustments has been “fixed” by further vigorous adjustments.

The “square wave” pattern in adjustments in Perth has been largely rectified.  The square wave is now in the difference between Acorn 1 and Acorn 2.

It beggars belief that a dataset that was proudly described as “world’s best practice” just seven years ago has needed to be adjusted by so much.  Has “best practice” changed so much?  How was Acorn 1 so wrong?  How can we be sure that the new version is better, and will itself not be changed again in a few years?

There are now four versions of WA temperature:  Raw; High Quality (no longer available); Acorn 1; and Acorn 2.  All are different.

The record for Western Australia reveals a state, not of excitement, but of confusion.

 

Next: the Northern Territory.

How Reliable is the Bureau’s Heatwave Service?

January 24, 2019

The Bureau of Meteorology presents heatwave assessments and forecasts in the interest of public health and safety.  Their heatwave definition is not based on any arbitrary absolute temperature, but uses a straightforward algorithm to calculate “excess heat factors”.  From their FAQs:

“Heatwaves are calculated using the forecast maximum and minimum temperatures over the next three days, comparing this to actual temperatures over the previous thirty days, and then comparing these same three days to the ‘normal’ temperatures expected for that particular location. Using this calculation takes into account people’s ability to adapt to the heat. For example, the same high temperature will be felt differently by residents in Perth compared to those in Hobart, who are not used to the higher range of temperatures experienced in Perth.

This means that in any one location, temperatures that meet the criteria for a heatwave at the end of summer will generally be hotter, than the temperatures that meet the criteria for a heatwave at the beginning of summer.

……

The bulk of heatwaves at each location are of low intensity, with most people expected to have adequate capacity to cope with this level of heat.”

Back in 2015 I showed how this algorithm works perfectly for Melbourne, but fails to detect heatwaves in Marble Bar and instead finds heatwaves at Mawson in the Antarctic.  In light of the long period of very hot weather across most of western Queensland, what does the Heatwave Service show?

Here is their assessment of conditions in Queensland over the last three days….

Fig. 1: Heatwave assessment for 21-23 January 2019

heatwave assessment

Most of inland Queensland has been in a “Low-Intensity Heatwave”, with a couple of small areas near the southern border of “Severe Heatwave”.

And here is their forecast for the next three days..

Fig. 2:  Heatwave forecast for 24-26 January 2019

heatwave forecast

Much the same, with a bit more Severe Heatwave coming.

So what were temperatures really like in the previous three days? Here’s the map for the middle of that period, Tuesday 22nd:

Fig. 3:  Maximum temperatures for 22 January

max 22 jan 1 day

About half the state was above 39 degrees C, a large area was above 42C, and there were smaller areas of above 45C.

And in the past week:

Fig. 4:  Maximum temperatures for 7 days to 23 January

max 22 jan 1 week

Average maxima for roughly the same areas were the same, except there was a larger area averaging over 45C!

This follows December when a large slab of the state averaged from 39C to 42C for the month.

Fig. 5:  Maximum temperatures for December 2018

max 22 jan 1 month

I’m focusing on Birdsville, circled on the map below (and indicated on the maps above.)

Fig. 6:  Queensland forecast towns- Birdsville indicated

qld map

Here are the maxima for Birdsville for January:

Fig. 7:  Birdsville Maxima for January

birdsville jan max

And here’s the forecast for the next 7 days:

Fig. 7:  Birdsville 7 Day Forecast

birdsville forecast

Apart from the 6th, when it was a cool 38.8C, since Christmas Eve the temperature has been above 40C every day, and is forecast to stay above 40C until next Tuesday (and above 45C until Sunday).  Minima have been above 25C on all but three days since Christmas.

And that’s a “Low Intensity” heatwave, with “most people expected to have adequate capacity to cope with this level of heat.”

The Bureau’s unspoken message?  It might be a bit hot, but you’re supposed to be used to it.  Harden up!

Western Queensland residents are pretty tough, but surely a month of such heat deserves a higher level of description than “Low Intensity”- especially for the vulnerable like babies, old people, and visitors.

This is worse than laughable.  The Bureau’s heatwave service is a crock.  As I said in my 2015 post, a methodology that fails to detect heatwaves at Marble Bar (or Birdsville!), and creates them in Antarctica, is worse than useless- it is dangerous.

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.

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.