Posts Tagged ‘climate’

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.

Townsville Rainfall In Context

February 11, 2019

The rain event which caused massive floods in Townsville (and fearful stock losses in the north-west) has now ended.  There have been some who have made further political capital out of this disaster by linking it to climate change.

According to Independent Australia, a “progressive journal”,

The City of Townsville, with some 20% of its suburban zones under water today (6 February 2019), might now be a model for the world — for possible climate change impacts and handling them. 

These days, the very heavy falls have been happening more frequently — for example, in 2007, 2009 and then in 2010.

Time for a reality check.

This has indeed been a record breaking event for Townsville.  A few graphs will illustrate.  Townsville airport has had its wettest 14 day period since 1941, averaging over 100mm per day.

Fig. 1:  14 day rainfall

Tville 14d rainfall

It has also broken the record for rainfall over 31 days:

Fig. 2:  31 day rainfall

Tville 31d rainfall

And with the wet season far from over, it is very likely to break the 121 day rainfall record.

Fig. 3:  121 day rainfall

Tville 121d rainfall

Townsville’s rain is very seasonal.  Annual rainfall averages about 1127mm, and half of that falls in January and February, with another quarter in December and March, so a plot of 121 day rainfall captures the relative strength of wet seasons over the years.  There doesn’t appear to be any recent increase in wet season strength.  What is interesting is there are periods of wetter and drier years, which is more plainly seen in a plot of decadal rainfall.

 Fig. 4:  Decadal rainfall at Townsville

Tville decadal rainfall

Rainfall appears to be in a decreasing trend.

But what about the claim for greater frequency of very heavy rain events?  Heavy rain events are usually short and intense, so three day rainfall will also show relative frequency and intensity.

Fig. 5:  Three day rainfall

Tville 3d rainfall

The “Night of Noah” in 1998 is obvious, and there was another intense event in 1953.  But there is NO trend.  (The calculated trend is zero.)  Intense events are not more frequent.  Similarly, the number of days per year recording 100mm of rain shows zero trend, even though there have been eight already this year.

Fig. 6:  Count of days per year with over 100mm of rain

Tville days over 100mm

There is no climate change signal in Townsville’s rain record.

Now, to show how different locations can lead to completely different interpretations of trends in climate, I turn to two locations in wetter parts of the tropics that I have some knowledge of.  I lived for many years not far from Pleystowe and Sarina Sugar Mills near Mackay, which are about 30 km apart.  Sarina appears to have an increasing trend in rainfall:

Fig. 7:  Decadal rainfall at Sarina

Sarina decadal rainfall

While Pleystowe shows no trend.

Fig. 8:  Decadal rainfall at Pleystowe

Pleystowe decadal rainfall

Notice the similar patterns of wetter and drier periods in Townsville, Pleystowe, and Sarina.

And incidentally, the most intense and highest rainfall events in these locations occurred many years ago, in 1990-91, the 1970s, the 1950s, and 1918.  As with the recent Townsville flood, these occurred when the monsoon trough, with embedded decaying cyclones, lingered overhead for many days or even weeks.

The Townsville flood was not due to climate change, but to a frequent North Queensland phenomenon- an intense monsoon trough stuck in one place for too long.  This was an unusually intense and long lasting example, but such events are not more frequent or more intense.

Another Inconvenient Pause

January 15, 2019

The Pause in global temperatures may be past, but here is another, longer Pause, and one that is much more difficult to explain: at ideal Australian sites, increasing greenhouse gas concentrations have led to a decrease in downwelling longwave radiation- the very opposite of expectations.

Basically, the theory behind the enhanced greenhouse effect is that the increase in concentrations of anthropogenic greenhouse gases leads to an increase in downwelling infra-red (IR) radiation, which causes surface warming.

Is there evidence for increasing downwelling IR in recent years, as atmospheric concentration of carbon dioxide has been rapidly rising?

The authors of Skeptical Science think so:

Surface measurements of downward longwave radiation

A compilation of surface measurements of downward longwave radiation from 1973 to 2008 find an increasing trend of more longwave radiation returning to earth, attributed to increases in air temperature, humidity and atmospheric carbon dioxide (Wang 2009). More regional studies such as an examination of downward longwave radiation over the central Alps find that downward longwave radiation is increasing due to an enhanced greenhouse effect (Philipona 2004).

Time for a reality check.

The links in the above quote do not work for me, so I use data available for Australia.

Greenhouse gas concentrations are measured at Cape Grim in north-west Tasmania.  According to the CSIRO,

The Cape Grim station is positioned just south of the isolated north-west tip (Woolnorth Point) of Tasmania. It is in an important site, as the air sampled arrives at Cape Grim after long trajectories over the Southern Ocean, under conditions described as ‘baseline’. This baseline air is representative of a large area of the Southern Hemisphere, unaffected by regional pollution sources (there are no nearby cities or industry that would contaminate the air quality).

Fig. 1:  Cape Grim Baseline Air Pollution Station (looking almost directly south)

c grim photo

Fig. 2:  CO2 concentration, Cape Grim.

co2 c grim

Fig. 3:  Methane concentration, Cape Grim.

ch4 graph

Fig. 4:  Nitrous oxide concentration, Cape Grim.

n2o graph

There is no doubt that concentrations of greenhouse gases have been increasing.  We should therefore expect to see some increase in downwelling longwave radiation.

Downwelling IR data are available from the Bureau of Meteorology which maintains a database of monthly 1 minute solar data from a network of stations around Australia, including Cape Grim.

What better location than Cape Grim to study the effects of greenhouse gas concentrations from month to month on readings of downwelling IR.  The instruments are within metres of each other under “baseline” conditions at a pristine site.

The data include 1 minute terrestrial irradiance (i.e. downwelling IR striking a horizontal surface) from which I calculated mean daily IR for each month.  To remove the seasonal signal, I calculate anomalies from monthly means.

Fig. 5:  Downwelling longwave radiation anomalies, Cape Grim.

ir over time capegrim

Oops! IR has been decreasing for the full length of the record, 20 years (May 1998 to June 2018).   And monthly IR anomalies plotted against monthly CO2 anomalies show a similar story:

Fig. 6:  Downwelling longwave radiation anomalies, Cape Grim.

ir vs co2 cgrim

In the most suitable location in Australia, from May 1998 to June 2018 there has been no increase in downwelling infra-red radiation, despite an increase of 41.556 ppm atmospheric concentration of carbon dioxide, 104.15 ppb of methane, and 14.472 ppb of nitrous oxide.

So what factors do influence downwelling IR and thus surface warming or cooling?  Together with solar radiation, that other greenhouse gas, H2O.  Gaseous H2O (humidity) and clouds formed of liquid and ice H2O are by far the major players in returning heat to the surface.

We see this in a plot of downwelling IR against cloudiness (from nearby Marrawa).

Fig. 7:  Downwelling IR anomalies vs Cloudiness, Cape Grim.

ir vs cloud capegrim

Daytime cloudiness (an average of observations at 9.00 a.m. and 3.00 p.m.) increases downwelling IR.  We have no data for night time cloudiness unfortunately.

To illustrate the irrelevance of carbon dioxide, here is a plot of anomalies of solar radiation (global irradiance), downwelling infra-red radiation, daytime cloudiness, and carbon dioxide concentration at Cape Grim over the past 20 years.

Fig. 8:  Anomalies of IR, Global Irradiance, CO2, and Daytime Cloud at Cape Grim 1998-2018

98 to 18 full range capegrim ir global co2 cloud anoms

And zooming in on 2008 to 2010:

Fig. 9:  Anomalies of IR, Global Irradiance, CO2, and Daytime Cloud at Cape Grim 2008-2010

98 to 18 2008 2010 capegrim ir global co2 cloud anoms

There is a feedback mechanism: cloudiness inhibits daytime temperature and increases IR and nighttime temperature; decreased cloudiness means decreased IR; but less cloud and higher daytime temperature will increase IR as well if sustained; and higher IR also increases daytime temperature.  Further, sustained decrease in global radiation due to increased cloud cools the surface, thus decreasing IR.

Carbon dioxide concentration changes have no detectable effect.

A desert location, where humidity is typically very low and rain and cloudiness very infrequent, would also be ideal for checking on downwelling IR from carbon dioxide.  Alice Springs in the central desert is such a location with available irradiance data.

At Alice Springs as well, since March 1995 downwelling IR has been decreasing.

Fig. 10:  Downwelling longwave radiation anomalies, Alice Springs.

ir over time alice

The relationship between cloud and IR is even more evident.

Fig. 11:  Anomalies of IR, Global Irradiance, CO2, and Daytime Cloud at Alice Springs 2008-2010

2008 2010 alice ir global co2 cloud anoms

Fig. 12:  Downwelling IR anomalies vs Cloudiness, Alice Springs.

alice ir v cloud

Cloudiness has an even greater influence on IR in desert than maritime locations.

TAKE AWAY FACT:-  For over 20 years, at what are arguably the most suitable sites in Australia, increasing greenhouse gas concentrations have had no detectable effect on downwelling longwave radiation.  Natural factors including cloudiness changes have vastly overwhelmed any such effect and have instead led to a decrease in downwelling longwave radiation.

That is indeed a most inconvenient pause.

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To replicate these findings:

Go to http://reg.bom.gov.au/climate/reg/oneminsolar/index.shtml

You will need to register with a username and password.  Then click on an irradiance observation station.  Select year and month.  Download the zip file, and open in your preferred application.  (I use Excel).  IR data are in Column W- the values are wattminutes of IR striking a horizontal surface of area one square metre.

My method:  Order the data in ascending order to remove null values.  Count the minutes of valid data and calculate the percentage valid of all possible minutes in that month.  (I discard months with less than 80% valid data.)   Divide the total minutes by 1,440 to convert to days.  Sum the valid data and divide by 60,000 to find kilowatthours; divide by the number of days to find the mean daily value; then multiply by 3.6 to convert to Megajoules.  Plot monthly values against time or carbon dioxide concentration.

Drought and Climate Change Part 2: Rainfall deficiency

September 7, 2018

In my last post, I looked at long term rainfall trends across Southern, South Eastern, and South Western Australia, and found no cause for alarm at recent rainfall decline.  Droughts can occur at any time and cause much hardship across wide parts of the country.  Global Warming Enthusiasts are gnashing their teeth, believing man-made climate change is making droughts worse.  Greg Jericho in the Guardian wrote last Thursday 30th August, “If you are a prime minister going out to the rural areas and you’re not talking about climate change, and you’re not suggesting that droughts are more likely to occur and thus farmers need to take greater responsibility, then you are failing in your job.”

Are droughts really “more likely to occur” with climate change, and is there any evidence they are becoming more frequent, more intense, and more widespread with global warming?

The Bureau of Meteorology says:

Drought in general means acute water shortage.

The Bureau’s drought maps highlight areas considered to be suffering from a serious or severe rainfall deficiency…. for three months or more….

……

  • Serious rainfall deficiency: rainfall lies above the lowest five per cent of recorded rainfall but below the lowest ten per cent (decile range 1) for the period in question,
  • Severe rainfall deficiency: rainfall is among the lowest five per cent for the period in question.”

This map of meteorological drought (areas in the lowest ten and five percent of 12 months rainfall to 31 August) shows the extent across Australia:

Fig. 1:  Recent 12 month Rainfall Deficiency Australia

12m drought map

Parts of central and southern inland Queensland, parts of eastern South Australia, many parts of New South Wales, and small areas of Victoria are in drought.  Notice that the droughted areas are separated by areas that are not in drought.

But, but… all of NSW is in drought, isn’t it?

100% of NSW has been drought declared, and 54.7% of Queensland, and indeed some parts are in a very bad way.   But “drought declaration” is the term the media, politicians, and general public don’t understand.  They assume that because 100% of NSW is drought declared, this means all of NSW is in drought.  Not so.  Drought declaration is a political or at best administrative instrument for giving drought assistance to farmers and communities.  Some areas of Queensland that have not yet been drought declared really are in the grip of drought; some “drought declared” areas of NSW are not in drought, as this map of NSW (6 months March to August) shows:

Fig. 2: 6 month Rainfall deficiency NSW

NSW map 6m

Of course the blank areas have had below average rainfall, which may turn into full blown drought, so the NSW government is being proactive.  However, they are not at this time in meteorological drought with serious or severe rainfall deficiency.

Trends in Drought Incidence

In the bigger picture, how widespread, how intense, how long lasting, and how frequent are droughts becoming in Australia?  For this analysis I use monthly rainfall data from 1900 to July 2018 from the Bureau of Meteorology at their Climate Change page, and calculate the number of months where the rainfall total of the previous 12, 18, 24, or 36 months shows severe deficiency (in the lowest 5 percent of all months since 1900) or serious deficiency (in the lowest 10 percent).  (I am looking at droughts that last at least 12 months, not just short dry spells, and 12 months total rainfall includes rain in all seasons.)

I do this for various regions, as shown on the map below.

Fig. 3:  Australian Regions

Climate regions

I have plotted the number of consecutive months where the 12, 18, 24, and 36 month totals are in the lowest 5% and 10% of their respective values since 1900, and calculated the trend in months per century of increase or decrease. There are 96 plots, so I will only show a couple of examples, and summarise the results in Table 1 below.

Table 1:  Trends in Drought Incidence (Months per 100 Years) for various Australian Regions

Trend table

A negative trend indicates decreasing drought incidence, shaded green; a positive trend indicates increasing incidence, shaded pink.

Australia wide, and in the regions of Northern and Southern Australia and the Murray Darling Basin, and South Australia as a whole, since 1900 droughts of all lengths have become less frequent, and because these are broad regions, less widespread.  There is no evidence that climate change is making droughts more likely to occur, except for smaller areas (Victoria, Tasmania, and SW Australia) which have an increasing frequency of droughts of all lengths.

36 month dry periods are more frequent in SW Australia, SE Australia, Eastern Australia, Tasmania, Victoria, (and interestingly Queensland, but only for <10% deficiency).

Some examples will illustrate the complexity of the picture.

Fig. 4:  Number of consecutive months per calendar year of 12 months severe rain deficiency: Australia

12m 5% Aust

Fig. 5:  Periods of 36 months serious rain deficiency: Australia

36m 10% Aust

In the past droughts of all lengths and severity were more widespread across Australia.

Fig. 6:  Periods of 36 months severe rain deficiency: Southern Australia

36m 5% Sthn Aust

Similarly, multi-year periods of severe rain deficiency were much more frequent and widespread across Southern Australia before 1950.  In the last 50 years there has been only one month where the 36 month total was in the lowest 5th percentile.

Fig. 7:  Periods of 12 months severe rain deficiency: New South Wales

12m 5% NSW

Fig. 8:  Periods of 36 months severe rain deficiency: New South Wales

36m 5% NSW

Fig. 9:  Periods of 12 months serious rain deficiency: New South Wales

12m 10% MDB

Fig. 10:  Periods of 36 months serious rain deficiency: New South Wales

36m 10% NSW

Across NSW, 4 months of 2018 had 12 month totals in the serious deficiency range, but none in the severe range.  Droughts of all severity and duration have become less frequent and widespread.  The Millennium Drought lasted longer but was less severe than the Federation Drought.

The Murray-Darling Basin lies across four states including most of NSW, and is Australia’s premier food and fibre producing region.  The current drought is affecting many areas in this region.

Fig. 10:  Periods of 12 months severe rain deficiency: Murray-Darling Basin

12m 5% MDB

Fig. 11:  Periods of 12 months serious rain deficiency: Murray-Darling Basin

12m 10% MDB

Fig. 12:  Periods of 36 months serious rain deficiency: Murray-Darling Basin

36m 10% MDB

We can conclude from these plots of the Murray-Darling Basin that this drought is patchy, and while nasty, is not the most intense or long lasting even in living memory, let alone on record, and that droughts are becoming less frequent and less widespread.

Fig. 13:  Periods of 36 months severe rain deficiency: Queensland

36m 5% Qld

Fig. 14:  Periods of 36 months serious rain deficiency: Queensland

36m 10% Qld

Queensland has little trend in frequency of drought with severe deficiency over three years but less severe droughts have been more frequent- due to the droughts of the 1990s and the Millennium drought.

Fig. 15:  Periods of 36 months serious rain deficiency: Victoria

36m 10% Vic

The Millennium Drought stands out as the longest period of widespread serious rain deficiency.

Fig. 16:  Periods of 36 months serious rain deficiency: South-West Australia

36m 10% SW Oz

Here we see that all but one month of all the 36 month periods of serious rain deficiency have occurred since 1970, reflecting the marked drying trend.  This really is an example of climate changing.

Winter rainfall

Fig. 17:  Winter Rainfall Deciles across Australia, 2018

winter rain 2018

According to the Climate Council, “Climate change has contributed to a southward shift in weather systems that typically bring cool season rainfall to southern Australia.”  However the usual areas affected by this southwards shift, Tasmania, south-west Victoria, southern South Australia, and most of the south-west of Western Australia, have had an average to above average winter.  Droughted areas are to the north.   The southwards shift of weather systems caused by Climate Change cannot be claimed to have any part in this drought.

Drought is a dreadful calamity wherever and whenever it occurs.  And on top of other difficulties in Queensland is the bureaucratic approval process under Vegetation Management regulations before graziers can push mulga to feed starving stock.

This drought may get worse if a full El Nino develops.  It is unlikely to break before six months or even 18 months.  By then it will be much more severe and widespread.  However, climate change has not caused this drought.  While there is evidence for increasing drought frequency and thus likelihood of more drought in the future in Tasmania, southern Victoria, southern South Australia, and the south-west of Western Australia, across the rest of Australia there is strong evidence that droughts have become less frequent, less severe, less widespread, and shorter.  If climate change is claimed as the cause of increasing droughts in the far southern regions, then climate change must also be causing less frequent droughts across the vast bulk of Australia, where droughts are always “likely to occur”, but not “more likely”.

Drought and Climate Change Part 1: Long Term Rainfall

September 1, 2018

The current drought conditions in New South Wales and large parts of Queensland are getting a lot of media attention, and of course the usual suspects are linking it to climate change and our apparently “unambitious” emissions targets in the NEG.  But are droughts really becoming “the new normal”, and are they becoming more frequent, more intense, and more widespread with global warming?

There are two aspects to consider: long term rainfall trends in various regions, and periods of rainfall deficiency.  In this post I will look at long term rainfall, and Part 2 will look at rainfall deficiency i.e. drought incidence.

Long term rainfall trends

Everyone “knows” southern Australia is getting drier.  Paul West in Feeding Australia Pt 2 on the ABC says there has been a 28% decrease in rainfall over the past 30 years.  The Climate Council says “Over the past 30 years, there has been a discernible decrease in rainfall across southern Australia.” That’s their headline; in the details the Climate Council’s June 2018 Fact Sheet says:

“Climate change has contributed to a southward shift in weather systems that typically bring cool season rainfall to southern Australia. Since the 1970s late autumn and early winter rainfall has decreased by 15 percent in southeast Australia, and Western Australia’s southwest region has experienced a 15 percent decline in cool season rainfall.”

Both are true, but both are only half true, and in fact the ABC and the Climate Council as usual lie by omission.

The whole story is more complex but shows a completely different, and much less dramatic picture.  Using data for cool season (April- September) rainfall from the Bureau of Meteorology we can check on different time periods.

Fig. 1:  Cool season rainfall, Southern Australia, 1988-2017

Cool rain Sth Oz 19882017

Yes, if this is what Paul West based his statement on, 2017 had about 28% less rain than in 1988.  I hope he didn’t- comparing single years would be pretty bad science.  However there has been a marked decrease in cool season rainfall over this period, so the Climate Council is quite correct.

However, Figure 2 shows the big picture- since 1900.

Fig. 2: Cool season rainfall, Southern Australia, 1900-2017

Cool rain Sth Oz 19002017

Oops! Rainfall has in fact increased over southern Australia.

The reason for the current gnashing of teeth is that “living memory” only goes back about 70 years, and we are comparing current conditions with those of a few decades ago.  Figure 3 shows the average rainfall for the 10 year periods up to 2017.

Fig. 3: 10 year average Cool season rainfall, Southern Australia, 1900-2017

Cool rain Sth Oz 19002017 10yrs

As you can see, the average rainfall of the 10 years 2008 to 2017 was about 7% less than in the 1950s, 1970s, 1980s, and 1990s, but more than the 1920s and 1930s, and nearly 10% more than the 10 years to 1947.  Of the 10 decades before this one, five had less rain and five had more.  Southern Australian cool season rainfall is not “the new normal”, it is in fact “the old normal”.

Let’s now look at South-East Australia, below 33 degrees South and east of 135 degrees East.

Fig. 4: Cool season rainfall, South-Eastern Australia, 1988-2017

Cool rain SE Oz 19882017

Again there is an obvious decrease in rain over the last 30 years.

Fig. 5: Cool season rainfall, South-Eastern Australia, 1900-2017

Cool rain SE Oz 19002017

There has been a small decrease in cool season rainfall over the whole 118 years.  Again there was a marked step up in rainfall from the mid-1940s.  The plot of 10 year averages shows this more clearly:

Fig. 6: 10 year average cool season rainfall, South-Eastern Australia, 1900-2017

Cool rain SE Oz 19002017 10yrs

There was a decreasing trend up to the 1940s, and a decreasing trend from the 1950s to now.  The current 30 to 40 year decrease is nothing new.

However, rainfall records for individual sites go back much longer.  What do these show?  Here is a plot of monthly rainfall for all months at Penola, in South Australia, starting in 1863:

Fig. 7: Monthly rainfall (all months January 1863 – December 2017) at Penola, S.A.

Penola rain monthly

A very long term decreasing trend.  Running 12 month totals show wetter and drier periods:

Fig. 8: 12 month running total rainfall (all months January 1863 – December 2017) at Penola, S.A.

Penola rain 12m

There were very severe droughts around World War 1 and the late 1960s, but a big step up in the 1950s.  This is more obvious in a plot of 10 year totals:

Fig. 8: 120 month running total rainfall (all months January 1863 – December 2017) at Penola, S.A.

Penola rain 120m

This site shows a very long term rainfall decrease, complicated by droughts and strings of wetter years, and a huge step up in the middle of last century.  This site is one of many of varying lengths in the High Quality Rainfall dataset.  Nearly all show the mid-century step up, some show a small long term increase, some show a small long term decrease.

I amalgamated all 84 stations, and here are the plots for all months. Firstly, the number of stations reporting:

Fig. 9: Count of all stations in S.E. Australia reporting, all months

All SE sites Count

There are a number of long term sites.  There were 50 sites in 1898, as in 2017 (several had not yet reported January 2018).

Note: the following plots are of naïve means: there is no area averaging.

Fig. 10: S.E. Australia monthly rainfall (all months)

SE Oz all months

Note a small increase.  Now 12 month running totals of these means:

Fig. 11:  S.E. Australia 12 month rainfall (all months)

SE Oz 12 months

Now the 10 year running total of monthly means, but since 1898 when the number of stations was the same as now:

Fig. 12:  S.E. Australia 120 month rainfall (all months)

SE Oz 120 months 1898

The mid-century step up is obvious, with a decline since then.  The 10 year rainfall to December 2017 is about what it was a century ago.

I now turn to South West Australia.

Fig. 13: Cool season rainfall, South-Western Australia, 1988-2017

Cool rain SW Oz 19882017

A very serious decline since 1988.

Fig. 14: Cool season rainfall, South-Western Australia, 1900-2017

Cool rain Sw Oz 19002017

As you can see, the decline has been around since 1900, but with a marked step down starting in 1968, with a steep but uneven decline since then.  10 year averages show this clearly.

Fig. 14: 10 year average cool season rainfall, South-Western Australia, 1900-2017

Cool rain SW Oz 19002017 10yrs

Conclusion:

The long term data show a complex picture of long term cool season rainfall decline in south-west and some parts of south-east Australia, while southern Australia as a whole shows a very small increase.  It is true that rainfall has declined, as the Climate Council and ABC claim, over the past 30 and 40 years in many parts, but that is only half the story.  The whole story is much less dramatic.  Rainfall has been declining for a long time in WA, and in south-east Australia has been declining in two stages, separated by a large step up in rainfall in the middle of last century.

The current low rainfall is not “the new normal” but entirely consistent with “the old normal” and should be seen as just plain “normal”.  This is Australia.  Get used to it.

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.

 

 

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.”