Posts Tagged ‘global warming’

ACORN-SAT 2.0: New South Wales- What a mess

April 10, 2019

This is the seventh 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.  The Bureau has published a new station catalogue with more detailed information, the adjustment summary for each station, plus lists of comparative stations for adjustments and all comparison stations for each site, with explanations of adjustment terminology.  Well worth a look.

See my previous posts for Western Australia, the Northern TerritoryQueensland,  South Australia, Tasmania, and Victoria for a general introduction.  It is important to highlight this paragraph on the new ACORN-SAT home page:

The purpose of updating datasets like ACORN-SAT is principally to incorporate data that has been recorded since the last analysis was released, as well as historical paper records that have been recently digitised. ACORN-SAT version 2 also incorporates the findings and recommendations of the Technical Advisory Forum, applies the latest scientific research and understanding and, where applicable, introduces new methodologies. The overall aim of the update to ACORN-SAT is to provide improved estimates of historical changes in climate.

As well, in the ACORN-SAT FAQs, the Bureau says:

“… The important question is not which one (version) represents the absolute truth, but whether those estimates produce wildly different results, and whether the range of estimates provides a reasonable guide to what has actually occurred.”

Therefore, the Bureau has set their own criterion for whether Acorn 1 and Acorn 2 are at all useful and valuable.  To repeat:

“whether those estimates produce wildly different results, and whether the range of estimates provides a reasonable guide to what has actually occurred.”

The Context – New South Wales

Figure 1 is a map of Australia showing all of the Bureau’s ACORN-SAT climate monitoring stations.  New South Wales is the oldest and most populous state with climates varying from semi-desert to montaine.

Figure 1:  Australian ACORN-SAT stations

NSW map all

There are 25 Acorn stations in the NSW BOM database.  Differences between Acorn 1 and Acorn 2 are summarized in the following sections.

Additional data

An extra 27 years of data have been digitised for Canberra, and 45 years for Moree, which has had an enormous effect on annual temperature trends (see below).  Some locations had changes to new sites, with Acorn 1 data merged to Acorn 2 data, including Tibooburra and Wilcannia.

Largest temperature differences

In maxima, changes to Acorn 1 daily data ranged from +8.3 ℃ at Scone in 1996 to -9.6 ℃ at Cabramurra in 1998 applied to individual daily figures.

Remarkably, there were NO changes from Acorn 1 to Acorn 2 at Gunnedah.

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

Cabramurra max adj

Minima adjustments ranged from -13.4 ℃ at Wagga Wagga in 1946 to +9.6 ℃ at Scone in 1996 on individual days but with many days adjusted by -2 ℃ or greater.

Figure 3:  Daily changes in minima from Acorn 1 to Acorn 2 at Wagga Wagga:

Wagga min diffs

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

Record temperatures

New record maxima were established at nine stations, with the highest at Bourke (48.9 ℃) while other stations’ record highs were unchanged or reduced.  There were two notable changes.  Figure 4 shows maxima at Sydney in 1939, where the record was increased by 2.5 ℃ to 47.9 ℃.

Figure 4:  Three versions of maxima at Sydney in 1939

Sydney record max

(The temperature was below 20 ℃ on 16th and 17th.)

Figure 5 shows Port Macquarie, whose record maximum was reduced by -4.1 ℃ from 48.1 ℃ to 44 ℃ in 1944.

Figure 5:  Two versions of maxima at Port Macquarie in 1944

PtMcquarie record max

There is NO daily raw data for any Port Macquarie site from 1921 to 1956 at Climate Data Online, so there is no way of replicating these adjustments.

Such “wildly different results” are beyond rational explanation.

New record low temperatures were established at 15 stations, and a new record low for Acorn stations was set, not at Cabramurra in the Snowy Mountains, but at Inverell in the north: -13 ℃.  Canberra’s minimum was reduced by 2.9 ℃ to -11.5 ℃.

Figure 6:  Three versions of minima at Inverell

Inverell record min

Raw minimum of -10 ℃ is cold enough.  Acorn version 1 had cooled this further by 1.4 ℃, but version 2 cools version 1 by another 1.6 ℃, making it three degrees cooler than the raw figure.  Strange things happen in the past!

Quality Control: especially minimum temperatures higher than maximum.

In Acorn 1, 15 out of the 25 stations had at least one example of minimum higher than maximum- including 12 times at Bourke and Sydney, 15 at Tibooburra, and 212 times at Cabramurra.  The worst example was minimum 2.2 ℃ above maximum in October 1913 at Tibooburra.  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.

That is almost how he “corrected” the worst NSW example in Acorn 1 (minimum 2.2 ℃ above maximum at Tibooburra).  Here is a plot of the raw data and changes made by Acorn 1 and Acorn 2 at Tibooburra in 1913.

Figure 7:  Tibooburra temperatures October-November 1913

Tibooburra DTR 1913

Acorn 1 maxima (orange line) were reduced too far below Raw (brown). Acorn 1 minima (grey) were too far above raw minima (light blue).  Result: garbage. Acorn 2 has changed maxima (dark red) back to 0.1 ℃ below the raw value, and reduced minima (dark blue) from 17 ℃ to 16 ℃.  This is not the “mean of the original adjusted maximum and adjusted minimum”- but at least the DTR is not negative.

The problem was caused by far too large adjustments to both maxima and minima, and was fixed by more arbitrary adjustments.

Not all Acorn 2 adjustments resulted in an increase in warming- in several, the warming trend was reduced.  For example, Figure 8 shows annual temperature trends at Sydney.

Figure 8:  Maxima Trends in Sydney 1910-2017

Sydney max ann trends

The warming rate of +1 ℃ per 100 years in Acorn 1 has been reduced to +0.79 ℃ in Acorn 2.

However, at Coffs Harbour the warming trend in minima was more than doubled, from +1.47 ℃ to +3.17 ℃ per 100 years.

Figure 9:  Minima trends at Coffs Harbour 1952-2017

CoffsHbr min ann trends

Figure 10 shows the effect of including an extra 27 years of data on annual trends at Canberra, with Acorn 1 adjusted downwards from 2011.

Figure 10:  Trends in Canberra minima 1914-2017

Canberra min ann trends

Acorn 1 starts in 1940.  Canberra’s warming trend has been increased from +1.48 ℃ to +2.18 ℃ per 100 years.

Conclusion:

There are no additional stations, but additional digitised data at several stations has a large impact on annual trends.  As well, several Acorn 1 stations closed and their data merged with data from new sites in Acorn 2.

Large differences between Acorn 1 and Acorn 2 daily data of many degrees Celsius are found at several stations.  Interestingly, no changes were made to Version 1 in Gunnedah maxima, and only a few in minima.

New record maxima were established at nine stations, with the remaining stations’ records being reduced or unchanged.  The largest increase was of +2.5 ℃ at Sydney, and the largest decrease was at Port Macquarie where the record high was reduced by -4.1 ℃.

The issue of instances of minima being higher than maxima caused by too vigorous adjustments at 15 stations (including 12 times at Bourke and Sydney, 15 at Tibooburra, and 212 times at Cabramurra) has been “fixed”- only seven years after the problem was pointed out.

Not all Acorn 2 adjustments resulted in an increase in warming- in several, the warming trend was reduced.  However, excessive adjustments have resulted in Coffs Harbour’s Acorn 1 minima trend of +1.47 ℃ per 100 years being more than doubled to +3.17 ℃ in Acorn 2.

The size of the adjustments only seven years after the “world’s best practice” dataset was launched, is incredible, and demands explanation.  The explanation that Acorn Version 2 “applies the latest scientific research and understanding and, where applicable, introduces new methodologies”, is beyond belief, as most datasets so far examined are vastly different from Acorn Version 1.  This is not incremental improvement.

In the ACORN-SAT FAQs, in the answer to:

“Why should the adjustments change, weren’t they correct the first time?”

the Bureau says:

“… The important question is not which one (version) represents the absolute truth, but whether those estimates produce wildly different results, and whether the range of estimates provides a reasonable guide to what has actually occurred.”

By their own words they have condemned themselves- “wildly different results” is exactly what has been produced.  Adjustments made in Version 1 were apparently made in error as they have been “corrected” by adjustments in version 2.  Will these adjustments be in error and corrected in version 3?

The Bureau officers responsible for Acorn version 2 appear to be blissfully unaware that they have made adjustments of up to 13.4 ℃ to temperatures in the dataset they proudly claimed to be world’s best practice just seven years ago.

What a mess.

I will next show a summary of Version 2 changes across the whole network, and then look at annual trends at all stations.

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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.

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.

Why are Australian Sea Levels Rising?

October 22, 2018

The answer, my friend, is blowin’ in the wind…. literally.

In brief…

  • At Sydney, the long term sea level rise is about 1 mm per year, with short periods of rapid increase and a long plateau of very small or zero trend in the second half of last century.  As Australia is geologically stable, it is likely that a similar pattern occurred all around the coast.
  • This gradual sea level rise is consistent with oceanic warming since the Little Ice Age, with fluctuations resulting from El Nino-Southern Oscillation (ENSO) changes.
  • Tide gauge data since 1990 from different locations show rises varying from 2.4 mm to 7.2 mm per year.  A significant proportion of this is due to ENSO wind circulation changes.
  • There is no sign of any unusual acceleration in Australian tide gauge data.

The Bureau of Meteorology maintains the Australian Baseline Sea Level Monitoring Project, with a number of tide gauges around the coastline, shown here:

Fig. 1:  Australian Baseline Sea Level Monitoring Project

MSL map

These sites have monthly data only from 1990, mostly later, and two (Thursday Island and Port Stanvac) have very limited data and were not used in this study.   I have used data for Mean Sea Level for all sites on the Australian coastline to find the current situation with sea level rise, and use the much longer dataset from Fort Denison in Sydney Harbour as well for a longer term perspective.  Figure 2 is a plot of all monthly data from all sites.

Fig. 2: Australian Mean Sea Levels

MSL plot abs

Points to note:

  • The mean is a measure of central tendency: the full tidal range is at least twice the values shown for each site.  Broome’s range is well over 11 metres.  Portland has a very small range.
  • An Australian average of these means is meaningless.
  • Each site has a seasonal signal which is not regular.
  • It is difficult to make any meaningful comparison.

However if we look at sites individually, we can at least compare any trends.  Figures 3 and 4 show MSL at sites with the greatest and least trends.

Fig. 3:  MSL at Hillarys

MSL plot abs Hillarys

Fig. 4:  MSL at Stony Point

MSL plot abs StonyPt

According to this very short record, the rate of Australian sea level rise varies in different locations, from a low of 2.4 mm per year in Bass Strait to 7.2 mm per year at Hillarys in Western Australia.  Why is this?

Australia is very stable geologically, and these tide gauges are carefully checked with levelling connections between them and Global Navigation Satellite System (GNSS) sites maintained by State land and survey departments.  Therefore differing rates of land movement are unlikely to be responsible.

We need to compare all sites, and as well remove the seasonal signal.  To do this I calculate monthly anomalies for each site, then plot the results in Figure 5.

Fig. 5:  Monthly anomalies for all Australian sites:

MSL plot all anoms

With the seasonal signal removed, the data show some roughly similar patterns for all sites.  I now plot the mean of these anomalies, to find an “average” Australian sea level trend.

Fig. 6:  Average of all MSL anomalies

MSL anoms trend

All sites show marked dips in 1997-98 and 2015-16, clearly shown in the average.  The influence of El Nino perhaps?  Figure 7 shows the mean of all MSL anomalies with the scaled Southern Oscillation Index (SOI).

Fig. 7:  Average of all MSL anomalies and SOI/200

Aust MSL and soi

My first response was “Wow!”  Next, sea level plotted against SOI:

Fig. 8: MSL as a function of SOI

MSL scatterplot all v soi

For every one point increase in the SOI, Australian sea level rises an average of 3.2 mm, and SOI change can account for more than a third of sea level rise.  Now we check how the SOI has behaved over the last 27 years.

Fig. 9:  Trend in SOI, 1991-2018

SOI plot trend

In this short record, the SOI has increased by about 8 points.

From this, we can deduce that a portion of the perceived sea level rise since 1991 is due to the influence of the El Nino- Southern Oscillation (ENSO), of which SOI is a strong indicator.

What mechanism could there be for this?  The SOI is calculated from the difference in atmospheric pressure between Tahiti and Darwin.  Darwin’s sea level is compared with the SOI in Figure 10.

Fig. 10:  Darwin MSL anomalies and SOI/100

MSL plot Darwin SOI

The match is very close, as the plot of MSL vs SOI shows:

Fig. 11:  Darwin MSL as a function of SOI

MSL plot Darwin vs SOI

SOI has about twice the effect on MSL at Darwin as it has on the Australian average, and more than half sea level rise can be accounted for by change in SOI.  Here’s my explanation:

During La Nina, when SOI is high, the northwest monsoon is strengthened, the monsoon trough penetrates further into northern Australia in summer with lower atmospheric pressure and stronger northwest winds.  This combination pushes the sea up against the northwest coast, raising the sea level.  In winter, the monsoon disappears and winds are predominantly from the east.  During El Nino, the monsoon is weakened and may fail completely.  Thus northwest winds are weaker and the sea level is markedly lower.

That’s all very well for Darwin and other sites in northern Australia, but take a look at Figure 12, which compares seal level at Darwin with Spring Bay, in southern Tasmania, and about as far from Darwin as you can get without a passport.

Fig. 12: MSL at Darwin and Spring Bay

MSL plot Darwin Springbay all

Note that in some (but not all) El Ninos (marked) Spring Bay sea level is also strongly affected.  Note also that sea level at Spring Bay appears to start rising again several months before Darwin, in other words before the SOI starts rising.

The 2015-16 comparison of anomalies shows the Spring Bay sea level at its lowest in September 2015, rising strongly and four months before Darwin’s.

Fig. 13: MSL at Darwin and Spring Bay 2015-16

Darwin SpringBay anoms 20152016

To understand this we need to consider circulation patterns as they change through the year and with ENSO events, and their effect on local sea levels.  The following plots show the absolute 2015-2016 monthly mean sea levels and the long term average for each month.

Fig. 14: MSL at Darwin 2015-16 compared with average monthly levels

Darwin abs 20152016

Darwin’s long term average sea level is highest at the peak of the Wet season (February – March) and lowest in the Dry (July – August).  In 2015, the high was reached in January and the low in July- both one month earlier- and the 2016 high was in March- one month later.  Below normal sea levels lasted from April 2015 to April 2016.

In contrast, Spring Bay’s average sea level is highest in the southern wet season (Winter-July) and lowest in the summer dry season (November to February).  In 2015 the high was reached in May and the low in September, and the 2016 high in May.

Fig. 15: MSL at Spring Bay 2015-16 compared with average monthly levels

Spring Bay abs 20152016

This happens at other sites in the southeast of Australia (from Portland to Port Kembla including Tasmania).

Fig. 16:  Australian sea level at sites in the north and southeast.

MSL plot Nth SE

Note that the same pattern applies: sea level is lower in strong El Ninos and rises before the north (in 1997-98 and 2015-16 but not so clearly in 2006-07).

A possible explanation is that circulation changes associated with the ENSO are not restricted to the tropics, although that is where the effects are largest and most visible. In normal (non-El Nino) years, the sub-tropical ridge moves north over the continent in winter, and the winter storms around the lows to its south bring rain and winds from the south-west quarter to the southern coast, particularly South Australia, Victoria, and Tasmania.  These winds cause the sea to pile up (by a few centimetres) against the southern coast.  In summer, the sub-tropical ridge moves south, rain bearing storms mostly pass to the south of the Australian region, and blocking highs in the Tasman Sea bring strong north-west winds across the south-east of Australia.  This causes sea level to fall.

In a strong El Nino, these conditions occur earlier, with a rapid retreat south of the sub-tropical ridge so that winter storms with south-westerly winds are fewer and weaker and sea level is lower in winter and spring.  Summer sea levels (November to January) are close to normal.

Figure 17 tests the response of sea level to barometric pressure at Spring Bay.

Fig. 17:  Spring Bay MSL anomalies as a function of barometric pressure anomalies

SpringBay MSL vs Press.jpg

The result is clear.  More than half of sea level change is due to pressure variation, which causes winds to change.

The effect is much greater at Darwin.

Fig. 18:  Darwin MSL anomalies as a function of barometric pressure anomalies

Darwin MSL vs Press

By the way, how much does increase in sea temperature affect sea level?

Fig. 19:  Spring Bay MSL anomalies as a function of temperature anomalies

SpringBay MSL vs SST

At Spring Bay, very little.  An increase of one degree could raise sea level by 17 mm, but R-squared of 0.033 is tiny compared with 0.527 for air pressure.

Whatever causes El Nino also causes the southern seasonal weather cycle to occur earlier, and sea levels rebound several months before they do in the tropics.

What of the longer term?

The Australian Baseline Sea Level Monitoring Project data are limited to sea levels since 1990, so the record is too short to make assumptions about long term sea level rise, and certainly not about the future.  There are longer datasets available however.  Sydney Harbour (Fort Denison) has data from 1914.

Fig. 20: MSL anomalies at Fort Denison (Sydney)

Sydney 1914 to 2018

That’s a long term sea level rise of 1 mm per year, or 104 mm in 100 years- a bit over 4 inches.  Now there has been an apparent “acceleration” since 1991, matching the data at nearby Port Kembla:

Fig. 21: MSL anomalies at Fort Denison (Sydney) 1991-2018

Sydney 19912018

But once again note the correspondence with the SOI:

Fig. 22: MSL anomalies and scaled SOI Sydney 1991-2018

Sydney 19912018 soi

A significant portion of the recent sea level rise at Sydney can be attributed to a short term rise in the SOI.

So is this recent rapid rise unique?  By calculating the trend in sea level over 10 year periods, we can see periods when sea level rise has accelerated or slowed in the past:

Fig. 23:  10 year running trend in MSL at Sydney

10yr trends MSLSydney

The most recent rise in sea level of 7 to 8 mm per year over 10 years is less than that of the rise to 1953, when sea level rose by 10 mm per year.

If you think 10 year trends are too short, Figure 21 shows 30 year trends at Sydney:

Fig. 24:  30 year running trend in MSL at Sydney

30yr trends MSL Sydney

The current 30 year trend is exactly the same as the trend to 1965:  2.4 mm per year.  For the 30 year period to the mid-1990s the trend was zero.

Conclusion:

Across all tide gauges of the Australian Baseline Sea Level Monitoring Project, a significant proportion of sea level rise since 1990 is due to circulation changes associated with the El Nino- Southern Oscillation.  The effect is much greater in the north and west, where sea level rise is highest, but also is evident in the south-east.

Sydney’s long term record tells us that sea level has been rising at an average rate of about 1 mm per year.  There have been short periods of rapid increase and a long plateau of very small or zero trend in the second half of last century.  As Australia is geologically stable, it is likely that a similar pattern occurred all around the coast.

This gradual sea level rise is consistent with oceanic warming since the Little Ice Age, with fluctuations resulting from ENSO changes.

There is no sign of any unusual acceleration in Australian tide gauge data.  In 100 years from now sea level could be expected to be 100 mm to 200 mm higher.  A sea level rise of 5 to 10 times this amount is purely speculative and not based on empirical data, but instead is based on the worst case scenario of computer models.

Tropical Cyclones and Global Warming: A Reality Check

September 15, 2018

Recently there was widespread media reporting of Queensland Emergency Services Minister Craig Crawford’s release of “a plan designed to help first responders get ready for future weather extremes.”

In the ABC Online report, these quotes from Mr Crawford are emphasised:

“There are plenty of people out there who are climate change sceptics… but the consensus is our fire seasons are getting hotter and longer and our flood and cyclone seasons are certainly getting stronger and more frequent.”

“If we’re going to have cyclones happening in parts of Queensland that they don’t normally happen right now it means that we’re going to have to expand all the areas where we have response training, capability and everything like that,” Mr Crawford said.

Cyclone seasons getting stronger and more frequent?  Cyclones happening in parts of Queensland that they don’t normally happen right now?  Time for a reality check.

The Bureau of Meteorology has a useful resource in its Southern Hemisphere Tropical Cyclone Data Portal  which shows the tracks of all cyclones since the 1969-1970 season.  By clicking on each track you find details of each.   This is the 2017/18 season:

Fig. 1:  Cyclones of the 2017/18 season

Cyclone portal

I have used it to look at all cyclones that have crossed the coast of Australia (and I have included TC Nancy which came very close and whose impact was strongly felt without actually crossing the coast.)  I have counted the cyclones that crossed the coast in every month from October 1969 to now, allocating them to those parts of the northern coastline that they predominantly affected- the north-west, Northern Territory, Gulf of Carpentaria and northern Cape York, north-east Queensland, south-east Queensland (south of the Tropic of Capricorn), and New South Wales.

So here are some facts to annoy our Global Warming Enthusiast friends, and to demonstrate how ill-informed our Emergency Services Minister is.

Fig. 2: Total number of cyclones per season

All cyclones Aust

There has been a decrease in the number of cyclones over the past 48 years, a rate of five less in 100 years.  There has been little change in Western Australian cyclones:

Fig. 3: Total number of cyclones per season hitting North-West Australia

All cyclones NW

Whereas there has been a very noticeable decrease on the east coast (Queensland and NSW):

Fig. 4: Total number of cyclones per season hitting the east coast

All cyclones East coast

which is well illustrated by this plot of cyclones crossing the Queensland coast south of the Tropic of Capricorn:

Fig. 5: Total number of cyclones per season hitting south-east Queensland

All cyclones SEQ

And these images of cyclone tracks are instructive:

Fig. 6: Cyclones of south-east Queensland 1969-1992

SEQ cyclones to 92

Fig. 7: Cyclones of south-east Queensland 1992-2018

SEQ cyclones since 92

Oswald, Marcia and Debbie crossed the coast north of the Tropic of Capricorn and were rain depressions by the time they reached the south-east.

The difference is obvious.  No cyclone has crossed the coast south of Yeppoon since TC Fran in 1992.  26 years without a cyclone- people (and Mr Crawford) forget we had three in 1971.  If we do get another one no doubt it will be blamed on climate change.

So what connection is there between temperature and cyclones?

Fig. 8:  Australian tropical cyclones as a function of sea surface temperature

All cyclones Aust vs sst trop

As temperatures go up, cyclones go down!

Fig. 9:  Australian tropical cyclones as a function of Southern Oscillation Index

All cyclones Aust vs soi

The SOI is an indicator of El Nino, La Nina, or neutral conditions.  According to the BOM, consistently below -7 indicates El Nino, and above +7 indicates La Nina.  It is obvious that there have been very few cyclones in seasons with El Nino conditions, with the vast majority in neutral or La Nina conditions, and higher SOI indicates greater likelihood of cyclones crossing the coast.  This is not new, and the Bureau makes this clear.

Fig. 10:  Tropical cyclones in La Nina years

BOM map la Nina

Fig. 11:  Tropical cyclones in El Nino years

BOM map el nino

Future trends:

The Bureau discusses future trends at length at http://www.bom.gov.au/cyclone/climatology/trends.shtml

but seems to base its conclusions entirely on climate models:

There remains uncertainty in the future change in tropical cyclone frequency (the number of tropical cyclones in a given period) projected by climate models, with a general tendency for models to project fewer tropical cyclones in the Australia region in the future climate and a greater proportion of the high intensity storms (stronger wind speeds and heavier rainfall).

This is the BOM plot of severe and non-severe cyclones, which includes all tropical cyclones from 90E to 160E south of the Equator, many of which remained well offshore.

Fig. 12: Severe and non-severe tropical cyclones

BOM graph

Is there any evidence for cyclones becoming stronger, if fewer?  According to the BOM’s history of cyclones, no.  This graph plots the number of cyclones rated as severe by the Bureau (<970 hPa central pressure at peak intensity- low pressure is a good predictor of wind speed).  Interestingly, Marcia and Debbie are not listed as severe, but are described as severe in their reports, and definitely were, so I have included them in the tally.

Fig. 13: Severe land-falling tropical cyclones

Severe cyclones Aust

And showing how the proportion of severe tropical cyclones as a percentage of all land-falling cyclones has changed:

Fig. 14: Proportion of land-falling tropical cyclones rated as severe

Severe cyclones Aust %

Tropical cyclones in the past 48 years have decreased in number and intensity, and the proportion of severe tropical cyclones has also decreased, although it is entirely likely that this situation could reverse due to natural variability.

The Government’s Response

The Queensland Government is concerned cyclones may strike further south than they currently do.  They have records of cyclones going back 150 years.  Many, many of them have affected south-east Queensland and NSW.

The worst natural disaster in recorded Australian history was in March 1899 when TC Mahina (the Bathurst Bay cyclone) killed 307 people.

Here are some other significant tropical cyclones recorded by the Bureau:

February 1893 a cyclone crossed near Yeppoon.  This led to the Brisbane River floods.

January 1918. The Mackay cyclone, which caused many deaths.  There was a large storm surge and a barometric pressure reading of 932.6 hPa in a private barometer, and less than 944.8 hPa at the Post Office as the flange on the instrument prevented the needle from going lower.  Inland rainfall caused the highest recorded flood in the Fitzroy River.

March 1918. The Innisfail cyclone.  The pressure dropped to 926 hPa at Mourilyan Sugar Mill.  There was a large storm surge.  Almost all buildings in the town were destroyed or badly damaged.

March 1949.  A cyclone struck Rockhampton and Gladstone.

1967 TC Dinah affected southern Queensland and NSW.  The pressure dropped to 944.8 hPa at Sandy Cape.

In Queensland, counting only those cyclones that have actually crossed the coast, not just approached, here is a list of tropical cyclones since 1970 (see Figure 6) that have struck south of the Tropic of Capricorn (Rockhampton or Yeppoon.)

February 1971 TC Dora

February 1972 TC Daisy

March 1972 TC Emily

January 1974 TC Wanda

March 1974 TC Zoe

February 1976 TC Beth

March 1976 TC Dawn

February 1981 TC Cliff

March 1992 TC Fran

TC Nancy (January 1990) came close but did not actually cross the coast.

TC Marcia in February 2015 crossed the coast near Shoalwater Bay before moving south over Rockhampton.

There is also an impressive list of cyclones which have caused deaths and wind, wave, and flooding damage in NSW.   These include cyclones from 1892.  Included are:

March 1939, TC crossed the coast at Cape Byron.

January 1950   The Sydney cyclone of 1950, when the pressure dropped to 988 hPa in Sydney.

February 1954, TC crossed the coast at Tweed Heads, where the pressure dropped to 973 hPa.

February 1957 TC crossed the coast south of Port Macquarie.

January 1967 TC Dinah caused a large storm surge in the Tweed River.

February 1967 TC Barbara crossed the coast near Lismore.

March 1974 TC Zoe crossed the coast just north of the border and travelled through northern NSW.

January 1990  TC Nancy did not cross the coast but passed about 50km east of Cape Byron.

The Reality

Contrary to Minister Crawford’s claim, and the media’s breathless and uncritical reporting, tropical cyclones in the past 48 years have decreased in number and intensity, and the proportion of severe tropical cyclones has also decreased.  Predictions of future trends are purely speculative.  The current 26 year lull in tropical cyclones hitting the south of Queensland and northern NSW is unusual.  In the past it was normal for cyclones to strike much further south than they do now.  We should not become complacent.

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.

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.

Fake Survey: Is the “World Scientists’ Warning to Humanity” a Hoax?

November 19, 2017

The “Second Notice” released last week, with 15,364 scientist signatories from 184 countries, might be a hoax or a clever student prank.

What is notable and peculiar about the list of Signatories and the follow up list of Endorsers is the omissions.  No Michael Mann.  No Gavin Schmidt.  No Naomi Oreskes.  No Tim Flannery.  No Lewandowski.

James Hansen is the only eminent name I recognise.

Following on from Jo Nova’s excellent post on the recent publicity surrounding the release by the Alliance of World Scientists of their second warning to humanity, I decided to have a closer look at the AWS warning article and its 15,362 signatories, their backgrounds, and their motivation- and also, how the survey was conducted and how the Signatories and Endorsements were collected.

What I found strange is that along with the hundreds of scientists of all descriptions are theologians, philosophers, citizen scientists, renewable energy advocates, artists, musicians, photographers, a high school student- and a homeopath.

I then turned to the Endorsers, those who agree with the warning article but weren’t amongst the original Signatories.

Along with the bona fide scientists, and assorted activists, photographers, and philosophers, we find 1 wholesaler (educated in “the school of life”); 1 elementary (primary) school teacher; and 2 naturopaths.

As with the Signatories to the article, several of these later supporters entered themselves multiple times e.g. Harvey Quamme, research scientist, entered himself 3 times; David Wood, molecular genetics, entered himself twice- there were more like him.  How many more?

So I began to wonder- how well are the respondents checked, and how difficult is it to add your name- or someone elses’s?

The answer to both is: not at all.

All you have to do, dear friends, is go to their home page:

http://scientistswarning.forestry.oregonstate.edu/

Home page

Note the invitation to scientists “from any scientific discipline (e.g. ecology, medicine, economics, etc.)”

And the stipulation that “scientists only” are invited to Endorse the article.

Then click on “Endorse the Article”, and enter your details, not forgetting to confirm you are not a robot, then click save.  Your name will be added to the list of those who endorse the article.

Create Endorser

(Yes, I entered Saint Nicholas.)

Just really who are these Signatories and Endorsers? I’ve never heard of any of them (apart from James Hansen).  Are they real scientists (or homeopaths)?  Or are many of them completely fictitious, but with many real concerned individuals duped into adding their names?  And have real individuals been entered without their knowledge or consent?  How would anyone know?

It is possible to copy the lists of names into a Word document and do a word search to find how many times a particular profession is mentioned.  But look more closely at the names in various professions.  In the list of original Signatories, the names appear to be credible.  However in the list of Endorsers are some very interesting names.

The article has been endorsed by some pretty heavy hitters: amongst those who include “physics” in their entry are Albert Einstein and Ernest Rutherford.   Musicians include John Lennon and Elvis Presley.  Florence Nightingale is a nurse.  Luke Skywalker is an astronaut.  Indiana Jones is an archaeologist.

And note the name of the first respondent on the list of Endorsers.

Endorsement aaskan

Aaskan, Yushal Raseev.  Get it?

If this was a real survey, why would that entry have been left there for all to see for many days?

Check for yourself- there are sure to be many more to find.

Has this been a well-crafted, gigantic student prank?  Have we all been fooled by this farce?

The “Second Notice” of the World Scientists’ Warning to Humanity is worthless.  At the very least the survey software- at least for the Endorsing the article, and probably for the original Signatories as well-  has no security system for preventing or checking fake entries, so no one really knows if the names are real or bogus, or how many legitimate scientists really do support the article.

We know how climate change promoters ever since Hansen in 1987 have used cunning stratagems (remember “Mike’s Nature trick”?) to fool people and convince them that global warming is real.  Perhaps the whole climate change scare is a clever student prank from the 1980s that developed into a meme with a life of its own and grew and grew- the biggest practical joke ever perpetrated.

Perhaps, but it is clear that the Viewpoint article in the journal Bioscience entitled “World Scientists’ Warning to Humanity: a second notice” by Ripple et al. (2017) has no credibility and must be withdrawn.

It is a joke.

The Pause Update July 2017

August 11, 2017

The complete UAH v6.0 data for July have been released. I present all the graphs for various regions, and as well summaries for easier comparison. I also include graphs for the North and South Temperate regions (20-60 North and South), estimated from Polar and Extra-Tropical data.

The Pause has ended globally and for all regions including the USA, Australia, and the Southern Hemisphere, except for Southern Extra-Tropics, South Temperate, and South Polar. The 12 month mean to July 2017 for the Globe is +0.35 C.

These graphs show the furthest back one can go to show a zero or negative trend (less than 0.1 +/-0.1C per 100 years) in lower tropospheric temperatures. I calculate 12 month running means to remove the small possibility of seasonal autocorrelation in the monthly anomalies. Note: The satellite record commences in December 1978- now 38 years and eight months long- 464 months. 12 month running means commence in November 1979. The y-axes in the graphs below are at December 1978, so the vertical gridlines denote Decembers. The final plotted points are July 2017.
[CLICK ON IMAGES TO ENLARGE]

Globe:

Pause July 17 globe

The Pause has ended. A trend of +0.53C/100 years (+/- 0.1C) since February 1998 is creeping up, but the 12 month means have peaked and are heading down.

And, for the special benefit of those who think that I am deliberately fudging data by using 12 month running means, here is the plot of monthly anomalies:

Pause July 17 globe mthly

Northern Hemisphere:

Pause July 17 NH

The Northern Hemisphere Pause has well and truly ended.

Southern Hemisphere:

Pause July 17 SH

The Pause has ended but temperatures for the last 19 years are rising very slowly.

Tropics:

Pause July 17 Tropics

The Pause in the Tropics (20N to 20S) has ended and the minimal trend is now +0.52C/ 100 years.

Northern Extra Tropics:

Pause July 17 NExt

The Pause has ended and the trend is increasing, but the slowdown since 1998 is obvious.

Northern Temperate Region:

Pause July 17 Nth Temp

Using estimates calculated from North Polar and Northern Extra-Tropics data, the slowdown is obvious.

Southern Extra Tropics:

Pause July 17 SExt

The Pause has weakened but still just persists, and 12 month means have peaked.

Southern Temperate Region:

Pause July 17 Sth Temp

Using estimates calculated from South Polar and Southern Extra-Tropics data, the Pause likewise persists but has shortened.

Northern Polar:

Pause July 17 NP

The trend has increased and will continue to do so even though 12 month means are falling rapidly.  The strong trend in Arctic temperatures is due to a step change from 1995 – 2002, and the strong 2015 – 2016 El Nino.

Southern Polar:

Pause July 17 SP

The South Polar region has been cooling (-0.12C) for the entire record. Although the 12 month means may have peaked, this cooling trend will slow over the next few months, and Global Warming Enthusiasts may start to get excited.

USA 49 States:

Pause July 17 USA 49

The warming trend is increasing.

USA 48 States:

Pause July 17 USA 48

Excluding Alaska the USA has only +0.23C/ 100 years warming.  This trend will increase however.

Australia:

Pause July 17 Oz

The Pause has ended, but the trend since June 1998 has reduced from +0.42C/ 100 years to +0.3C, and since September 2002 is +0.13C.

The next graphs summarise the above plots. First, a graph of the relative length of The Pause in the various regions:

Pause length July 17

Note that the Pause has ended by my criteria in all regions of Northern Hemisphere, and consequently the Globe, and the Tropics, but all southern regions have a Pause for over half the record, including the South Polar region which has been cooling for the whole record. Note that the Tropic influence has been enough to end the Pause for the Southern Hemisphere, and the Pause is likely to disappear from all southern regions except South Polar in the next couple of months.

The variation in the linear trend for the whole record, 1978 to the present:

Trends 1978 july 17

Note the decrease in trends from North Polar to South Polar.

And the variation in the linear trend since June 1998, which is about halfway between the global low point of December 1997 and the peak in December 1998:

Trends 1998 july 17

For 19 years “global” warming has been dominated by the influence of the Tropics and North Polar regions.

The imbalance between the two hemispheres is obvious.

The Pause has disappeared from the USA, Australia, and the Southern Hemisphere, but not the Southern Extra-Tropics, South Temperate, and South Polar regions.  Interestingly, July anomalies have decreased in Northern regions but increased in Southern regions and the Tropics.  The next few months will be interesting.