Archive for November, 2015

Rainfall and Temperatures Part 2

November 29, 2015

In my last post I showed how in Australia more than three quarters of the difference between surface maxima and tropospheric anomalies can be explained by variation in rainfall alone. Figure 12 from that post showed that clearly:

Fig. 1: 12 month rainfall vs surface maxima – TLT difference: Australia

max diff v rain scatterplot

In this post I am looking at the different contributions made by the north and the south of Australia. This map shows the regions used for climate analysis by the Bureau.

Fig. 2: Climate regions

Climate regions
Northern Australia and Southern Australia have vastly different climates, as shown by the following graphs of mean monthly rainfall:

Fig. 3: Mean Monthly Rainfall: Northern Australia

Nthn rain av

Fig. 4: Mean Monthly Rainfall: Southern Australia

Sthn rain av

The sheer volume of wet season rain in the north dominates, and distorts, the national means, therefore it is sensible to analyse northern and southern influences separately.

Northern Australia, north of 26 degrees South, has less than 3 degrees outside the Tropics, so may be considered the tropical northern half of Australia. Do not think of this as a lush tropical paradise. Far from it. There are a couple of very small areas of wet tropics, and some softer country in the far south-east, but the rest is either monsoonal wet/dry (very wet summers, almost completely dry for the rest of the year), or desert. The rainfall graph for Northern Australia is for the whole region- most of which is desert. If the monsoon is weak or fails altogether we get drought.

Southern Australia is largely influenced by the Southern Annular Mode. Each winter the southern high pressure systems move north, and cold fronts sweep across the south bringing winter rains. If these systems don’t move far enough north, the rain systems dodge the bottom of the continent, resulting in drought conditions. The far north-east of Southern Australia (southern Queensland and northern New South Wales) gets a wet summer/ dry winter pattern, and Tasmania and coastal New South Wales get rain in all seasons.

Comparing the influences of northern and southern rainfall on the national surface- TLT differences:

Fig. 5: Northern rain vs national maxima- TLT difference

Nthn rain v nat max diff

More than two thirds of the national maxima-TLT difference can be explained by variation in the northern rainfall alone.

Southern rainfall has a much weaker correlation with national maxima- TLT difference:

Fig. 6: Southern rain vs national maxima- TLT difference

Sthn rain v nat max diff

Now let’s plot Northern rain vs northern maxima- TLT (for the whole of Australia) difference, firstly for each month:

Fig. 7: Northern rain vs northern maxima- national TLT difference: monthly

Nth rain v nth diff monthly

Nearly two thirds of the monthly difference can be explained by monthly rainfall alone.

Fig. 8: Northern rain vs northern maxima- national TLT difference: 3 monthly

Nth rain v nth diff 3m

Three quarters of the 3 month mean surface maxima minus national TLT difference can be explained by rainfall.

Fig. 9: Northern rain vs northern maxima- national TLT difference: 12 monthly

Nth rain v nth diff 12m

R squared value of 0.8348 corresponds to a correlation of -0.91! But wait- there’s more! The 24 month means give an even better fit!

Fig. 10: Northern rain vs northern maxima- national TLT difference: 24 monthly

Nth rain v nth diff 24m

And 120 month means show an extremely close fit:

Fig. 11: Northern rain vs northern maxima- national TLT difference: decadal

Nth rain v nth diff 120m

97% of decadal northern surface maxima- national TLT difference is explained by decadal northern rainfall variation.

Fig. 12: Southern rain vs southern maxima- TLT difference: monthly

Sth rain v Sth diff monthly

Fig. 13: Southern rain vs southern maxima- TLT difference: 3 monthly

Sth rain v Sth diff 3m

Fig. 14: Southern rain vs southern maxima- TLT difference: 12 monthly

Sth rain v Sth diff 12m

More than half the difference between southern Australian maxima and TLT can be explained by southern rainfall variation.

However, 24 month means are not as good a fit:

Fig. 15: Southern rain vs southern maxima- TLT difference: 24 monthly

Sth rain v Sth diff 24m

And the long term means are a much poorer fit:

Fig. 16: Southern rain vs southern maxima- TLT difference: decadal

Sth rain v Sth diff 120m

Only 34% of the decadal southern Australian maxima-TLT difference is due to rainfall variation.

In tabular form:

Fig. 17: Range of rainfall anomalies and R-squared values for regional rainfall vs regional surface maxima- national TLT differences

Table rain r2

Conclusion:

Australian climate is dominated by the tropics, and tropical rainfall variation dominates the national surface- troposphere differences, and even more so the tropical surface – national troposphere temperature differences: the greater the rainfall variation, the greater the difference between surface and tropospheric temperatures.

For a better analysis, we would need UAH anomalies for Australia separated into north and south of 26 degrees South.

Why Are Surface and Satellite Temperatures Different?

November 20, 2015

Many are puzzled by the difference between surface temperature, measured in Stevenson screens, and atmospheric temperature, as measured by satellites. Some sceptics suspect surface temperatures cannot be trusted; some global warming enthusiasts claim satellite data are not accurate. The truth is both are accurate enough to be useful for their own purposes. But why the difference?

I have used data from the Bureau’s Climate Change Time Series site for monthly rainfall and surface temperatures for Australia, and from University of Alabama-Huntsville (UAH) for Temperature of the Lower Troposphere (TLT) anomalies for Australia, from December 1978 to October 2015. I converted rainfall and surface temperatures to anomalies from monthly means 1981 – 2010, the same as UAH. Throughout I use 12 month running means.

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

The context:

This map shows the location of the Acorn surface temperature observing sites.

Fig.1: ACORN-SAT sites

Acorn network

Note the scale at bottom left, and that they are concentrated in the wetter, more closely settled areas. As with rainfall observing sites:

Fig.2: Rainfall observation sites

Rainfall network awap

Fig.3: mean annual rainfall:

Avg ann rain map

The scale is in millimetres: divide by about 25 to get inches. Consequently a very large area of Australia is desert, and another large area is grassland with few or scattered trees. Very little of Australia is green for more than a few months of the year. More on this later.

The data:

Here are 12 month running means of the Bureau’s Acorn maxima and minima since December 1978.

Fig. 4: 12 month running means of monthly maxima and minima anomalies (from 1981-2010 means) for Australia

Max v min

Note that minima frequently lags several months behind maxima- which is why mean temperature doesn’t give us very much useful information.

Now compare surface temperature with the lower troposphere:

Fig. 5: Minima vs TLT anomalies

min v uah

Fig. 6:  Maxima vs TLT anomalies

max v uah

TLT approximately tracks surface temperature, but with smaller variation. So what causes the difference between surface and atmospheric temperatures?

The culprit is that wicked greenhouse gas, H2O.

In the following graphs 12 month mean rainfall is scaled down by a factor of 25, and inverted: dry is at the top and wet is at the bottom of these plots.

Fig. 7: Maxima vs Inverted Rain

max v rain

It is plainly obvious that very wet periods mostly coincide with low maxima, and dry periods with high maxima.

Fig. 8: Minima vs Inverted Rain

min v rain

Again, minima has no immediate relation with rainfall (although cloudy nights are warmer), lagging many months behind.

Next I calculate the difference in anomalies- surface temperature minus TLT- to analyse the difference between surface and satellite data. As minima lags many months behind rainfall a close relationship is not expected.

Fig.9: Acorn minima anomalies minus TLT anomalies compared with rainfall anomalies

min diff v rain

However, Acorn maxima minus UAH matches rainfall remarkably well.

Fig.10: Acorn maxima anomalies minus TLT anomalies compared with rainfall anomalies

max diff v rain

It is not an exact match of course. The next graph plots the surface maxima- TLT anomaly difference against 12 month mean rainfall anomaly sorted from smallest to largest, with the horizontal axis showing monthly percentile rank by rainfall:

Fig.11: Comparison of maxima-TLT anomaly difference with ranked rainfall anomalies

Max-UAH v rain%

Note that the surface- atmosphere difference tracks rainfall quite closely (+/- about 0.5C), with the largest positive and negative differences at the rainfall extremes, and also that the 12 month period where the rainfall anomaly crosses from negative to positive is at the 59th percentile: there are more dry months than wet months.

Another way of showing the relationship is with a scatterplot:

Fig.12: Surface maxima- TLT difference compared with rainfall

max diff v rain scatterplot

Note the R squared value: 0.76! At least three quarters of the difference can be explained by rainfall variation alone- not bad across a whole continent with a northern wet summer / dry winter and a southern wet winter / dry summer pattern.

An over simplified explanation of a complex process:

In wetter than normal weather, more and thicker clouds reflect sunlight and shade the surface, keeping it cooler than normal. Moisture from the surface (and vegetation) is evaporated, also cooling the surface. Deep convective overturning occurs during the day and evaporated moisture ascends in the atmosphere, where it condenses, releasing heat. The troposphere anomaly is thus relatively warmer than the surface anomaly in moist conditions such as during wet weather.

In a drought, fewer clouds allows more sunlight to heat the surface. The ground is dry; surface water is scarce; vegetation is thinner, drier, and shades less of the ground. Therefore the surface is hotter than normal. Less evaporated moisture means less condensation releasing heat in the troposphere, and therefore the troposphere anomaly will be relatively cooler than the surface anomaly.  As well, as the Bureau explains, ” the rate at which temperatures cool with increasing altitude (known as the lapse rate) is greater in dry air than it is in moist air.”  Thus in dry weather, ignoring convection, the atmosphere will be cooler than normal.

Yes, but…

So how does this explain why the October 2015 surface maximum anomaly was a record +3.08C above the 1981-2010 mean, while the UAH anomaly was a mere +0.71C, and the rainfall anomaly was only -12.75mm, nowhere near the lowest?

This map shows the Normalised Difference Vegetation Index for October. The Bureau explains the index as a measure of “the fractional cover of the ground by vegetation, the vegetation density and the vegetation greenness”.

Fig.13: Normalised Difference Vegetation Index (NDVI) October 2015

Vegetation Oct 2015

What do the dark brown areas look like on the ground? Here’s a photo I took recently around about the area circled red:

Fig.14: Droughted country, Western Queensland, September 2015

Bare, dry dirt with scattered tussocks of dead grass- scattered prickly acacia in the distance.

A large area of Australia is relatively bare and bone dry, therefore hotter. Over wide areas, much less moisture is convected into the atmosphere, which will thus be relatively cooler than surface anomalies. North winds blowing from the interior towards the south will bring hot dry air even to green areas, causing much hotter surface temperatures there as well. Much of the moisture evaporated from these wetter areas is blown out to sea (outside the UAH Australian grids) so the TLT over even these green areas is relatively cooler than expected.

Conclusion:

Atmospheric temperature anomalies are necessarily different from surface anomalies. Usually, atmospheric anomalies are less than surface maxima in hot periods and higher than surface anomalies in cool periods.

There is no conspiracy: over three quarters of the difference between surface and atmospheric temperature anomalies is due to rainfall variation alone.

The Pause: October 2015 Update

November 11, 2015

UAH v6.0 data for October were released on today.  Here are updated graphs for various regions showing the furthest back one can go to show a zero or negative trend (less than +0.01C/ 100 years) in lower tropospheric temperatures.  The strongest El Nino since 1997-98 has struck down its  first victim!  There is now NO pause in the Northern Hemisphere data.  However, in some regions it has lengthened.  Note: The satellite record commences in December 1978.  The entire satellite record is 36 years and 11 months long.

[CLICK ON IMAGES TO ENLARGE]

Globe:

Zero trend oct 2015 globe

There has been zero trend for over half the record.

Northern Hemisphere:

It might be something I’m getting wrong in my calculations, but the Pause has suddenly disappeared.

The trend since December 1997 is +0.17C/ 100 years +/-0.1C.

Southern Hemisphere:

Zero trend oct 2015 SH

The Pause has extended by several months.  For more than half the record the Southern Hemisphere has zero trend.

Tropics:

Zero trend oct 2015 tropics

Unchanged from last month.

Tropical Oceans:

Zero trend oct 2015 tropic oceans

Unchanged from last month.

North Polar:

Zero trend oct 2015 N Polar

The Pause has lengthened by one month.

South Polar:

Zero trend oct 2015 S Polar

For the whole of the satellite record, the South Polar region has had zero or negative trend.  So much for a fingerprint of warming due to the enhanced greenhouse effect being greater warming at the Poles!

Australia:

Zero trend oct 2015 oz

The Pause has lengthened by two months.

USA 49 states:

Zero trend oct 2015 USA49

Four months shorter.

And now, in breaking news, for those who were waiting to hear whether satellite data confirm October 2015 as Australia’s hottest ever………

Oz October rankings

Sorry, but this October ranked 12th out of 37.  It made the hottest third (just).

The Pause continues.

Case Studies in “World’s Best Practice” 2: Kerang

November 5, 2015

Introduction:  This series of posts is intended to show that despite Greg Hunt’s loyalty, all is not right at the Bureau of Meteorology.

Please refer to my first post, Case Studies in “World’s Best Practice” 1:  Wilsons Promontory, for a complete description of the Bureau’s claims, the problems, data sources, and my methods.

Here are some further examples of “World’s Best Practice”.

********************

Kerang is on the Murray River, about 250 km from Melbourne.  The story of temperature adjustments here illustrates much that is wrong with the Bureau: misinformation, incompetence, lack of transparency, and unscientific behaviour.  This post took longer than expected because the more I looked, the more problems I found.

Note: Both maxima and minima at Kerang are warming. I have no comment on whether the adjustments are justified.  I am only interested in the methods used.

Problem 1: Missing data

The Bureau’s claim that they provide raw data as well as adjusted data is a half-truth, and completely misleading- some would say, dishonest.

The Bureau has adjusted Kerang maxima at 01/06/1957 and 01/01/1922, and minima at 18/01/2000 and 01/08/1932, and provides daily adjusted temperatures from 1/1/1910.

Unfortunately, there are NO daily raw data for Kerang before 1/1/1962.

Where are 52 years of daily temperatures?  How is it possible to have adjusted digitised data but no raw digitised data for half of the record?

Another issue brought to my attention is that there is an enormous amount of data missing even from Acorn: a large proportion every year before 1960, especially from 1932 to 1949, when 100 to 180 days are missing every year.

null days kerang

This lack of transparency makes it impossible to replicate and analyse the adjustments at Kerang.  If it can’t be replicated, with all data made available, it isn’t science.

Problem 2: Nonsense temperatures

There is only one instance when Acorn shows that the minimum temperature, the lowest temperature for the 24 hour period, was higher than the maximum temperature.

min max kerang

That dot at ‘0.6’ shows that on 2nd February 1950 the coldest temperature was 0.6C hotter than the hottest temperature!  Unfortunately it is impossible to compare with the missing raw data.

Any organisation that can’t perform a basic quality control test on its product is incompetent, as is any Review Panel or Technical Advisory Forum that endorses it.

Problem 3: Artificial warming 

Even though UHI makes Melbourne unsuitable for use in climate analysis, the Bureau still uses it to adjust the early data at Kerang!

Problem 4:  Neighbours

One of the neighbours used to adjust Kerang is Broken Hill, 477 km away, and another is Snowtown in South Australia, 565 km away.

Problem 5:  Results of adjustment

Comparison of differences between Kerang and its neighbours, pre- and post adjustment, using annual temperatures.

Firstly, minima, from the 2000 adjustment: Kerang minus neighbours, annual anomalies from 1985-2014.

Kerang comp 2000 min

The adjustment of -0.4C applied to years before 2000 is too great.  The slope of the mean difference from the neighbours is much too steep.

Next, for the 1932 adjustment (annual anomalies from 1917-1946 means):

Kerang comp 1932 min

Again, the adjustment is too great, as they make the differences from neighbours much greater.

The same pattern follows with maxima.  The 1957 adjustment (anomalies from 1944-1973):

Kerang comp 1957 max

And the 1922 adjustment (anomalies from 1910-1938):

Kerang comp 1922 max

In both cases Kerang is cooling compared with neighbours, but the adjustments reverse this and make Kerang compare less well with its neighbours.

Problem 6:  Undocumented adjustments

The Bureau lists only two adjustments to minima at Kerang:  -0.4 on 18/01/2000 and -0.61 on 01/08/1932.  This is not the whole story, as a plot of the actual annualised adjustments shows:

Kerang adjustments min

If the adjustments were as stated, the difference between adjusted and raw temperatures would be indicated by the blue lines.  The actual adjustments are shown by the brown lines.

The queried adjustments are not mentioned in the Bureau’s list here.

Similarly, there are two documented adjustments to maxima: -0.71 on 01/06/1957 and +0.33 on 01/01/1922.  These are visible in the next graph, but note the extra adjustment before 1950, and a series of adjustments from 1948 back to 1925.

Kerang adjustments max

I understand why these are needed: to adjust for the steadily increasing difference between Kerang and neighbours in this period.  But why was this not documented?

Thus we see at Kerang further misinformation and lack of transparency through failure to supply digitised raw data to allow replication; incompetence through not using basic checks for data integrity, resulting in publication of the “world’s best practice” temperature dataset with minimum temperatures higher than maximum; use of UHI contaminated sites when making adjustments; use of distant neighbours from different climate regimes; over-zealous adjustments resulting in worse comparison with neighbours than before; and undocumented adjustments.

Half-truths, incompetence, lack of transparency, and unscientific practices are evident at many other sites.  A proper investigation into the Bureau is overdue.

Pacific Sea Levels- Warming, ENSO, or Wind?

November 1, 2015

Apparently our Opposition Leader Bill Shorten, his deputy Tanya Plibersek, and immigration spokesman Richard Marles are heading off to the Pacific to discuss “the dangerous consequences of climate change”, all paid for by media baron Harold Mitchell, according to yesterday’s Weekend Australian.
They will visit Papua New Guinea, the Marshall Islands, and Kiribati (pronounced “Kiri-bahss”).
The President of Kiribati leads the complaints about the threat of global warming to his island nation. Kiribati has indeed seen foreshore erosion and salt water intrusion in recent years- just don’t mention causeway construction and underground water extraction.
Time for a reality check.
Sea level rise in Kiribati and the Marshalls has nothing to do with climate change and everything to do with the ENSO cycle, and winds in particular.
In this post I use data from the Australian Bureau of Meteorology’s Pacific Sea Level Monitoring Project at http://www.bom.gov.au/pacific/projects/pslm/. I also use NINO 4 data from http://www.cpc.ncep.noaa.gov/data/indices/sstoi.indices and Trade Wind Index data from http://www.cpc.ncep.noaa.gov/data/indices/wpac850.
Fig. 1: Island nations in the Pacific Sea Level Monitoring Project, also showing the area of the NINO 4 index. (Click graphics to enlarge).

Pacific MSL map
I have converted raw mean sea level data to monthly anomalies from 1995-2014 means, and scaled down NINO 4 and Trade Wind data, in order to make comparison easier.
This figure shows sea level data at all of the islands in the BOM’s Pacific Sea Level Monitoring Project.
Fig. 2: 12 month smoothed sea level anomalies, 1992-2015, for all islands in the Sea Level Monitoring Project. The vertical axis is in metres.

MSL graph all
Point 1: While there is broad agreement on rises and falls, the timing of the rises and falls is very mixed- some rise at the same time as others fall.

This is clearly shown by Kiribati and the Marshalls- when one is rising the other is falling.
Fig. 3: 12 month sea level anomalies at Kiribati and Marshalls, 1992 to 2015.

MSL M & K
Perhaps Mr Shorten can discuss why sea level changes at the Marshalls precede those at Kiribati by many months, such that they are presently moving in opposite directions.
Point 2: There is no doubt that from 1992 to 2015, throughout this region sea levels have been rising: both the Marshalls and Kiribati by 4.8 mm per year. However, since sea level rise occurs at different times, it cannot be due to temperature change. This is further reinforced by the next graphic.
Fig. 4: NINO sea surface temperature anomalies, January 1982- September 2015, 12 month means.

NINO indices
NINO 4 sea surface temperature anomalies since 1982 have almost zero (+0.01C per decade) trend (and the other indices show negative trend in sea surface temperatures). The tropical Pacific has not warmed.
At first glance, sea level change at these islands appears to correlate well with El Ninos and La Ninas, however close analysis shows a much more complex picture.
Fig. 5: 12 month running means of Kiribati sea level anomalies compared to NINO 4

K MSL v NINO4
Note that sea level changes several months before the NINO 4 index.
Fig. 6: 12 month running means of Marshall Islands sea level anomalies compared to the Trade Winds Index.

Tr Winds v MSL Marshalls
Note that the Trade Winds mostly change simultaneously with or some months before sea levels change.
Point 3: Sea level changes at Kiribati precede ENSO events, as measured by the NINO 4 index, but follow or match the Trades at the Marshalls (just to the north of the NINO 4 region).
However, in Figure 3 above, note that sea level anomalies at the Marshalls PRECEDE Kiribati.
So what is the cause of sea level rise in the western Pacific, which is an alarming 4.8mm per year at Kiribati and the Marshall islands?
Fig. 7: Trade Wind Index, December 1992 to September 2015

Trades trend
The Trade Winds have been increasing throughout the period of the Sea Level Monitoring Project, pushing surface water from east to west across the Pacific.
Fig. 8: Western Pacific winds at 1 November 2015

Pacific winds 1Nov 15(From http://earth.nullschool.net/#current/wind/surface/level/orthographic=163.83,1.50,277 )
At Kiribati westerly wind pulses, which help to initiate and strengthen El Ninos, push water against the low coral cay and raise the sea level at the tide gauge, (located on the western side of Tarawa, which is in normal and La Nina years the leeward side).

 

Fig. 9:  Tarawa Atoll

Tarawa atoll
When these winds slacken and trade winds strengthen, the sea level drops. In the rising sea level phase, the westerly winds push warmer water eastwards, and the weaker trades do not bring in as much cooler water from higher latitudes. Thus the rise in sea level at Kiribati precedes a rise in sea surface temperature, and the peak in sea level occurs about 5 months before the peak in NINO 4, one of the El Nino indicators.
Conversely, weaker trade winds bottom out some months before the bottom of sea levels at the Marshalls, which are to the north of the area affected by westerly wind pulses.
Therefore we can reassure Mr Shorten and his pals that sea level rise has little to do with climate change (unless alarmists claim that global warming will lead to stronger La Ninas, which lead to cooler temperatures- but I thought the plan was for more El Ninos).
Sea level rise is largely due to wind. And no doubt Mr Shorten will contribute to that.