Posts Tagged ‘minimum temperatures’

Downwelling Infra-Red Radiation and Temperature: Part 2

February 7, 2020

In Part 1 I showed that:

  • Downwelling infra-red radiation (so called “back radiation”) is real and measurable including at night.
  • It is greatly increased by cloud and humidity,
  • It results from daytime heating of the ground, which then loses heat by conduction, convection, evaporation, and radiation, into the atmosphere where the IR is repeatedly absorbed and re-emitted in all directions by greenhouse gases (including water vapour).
  • A warmer atmosphere from whatever cause, natural or enhanced, will result in greater downwelling IR.

In this post I will look at the relationship between downwelling IR and temperatures at five Australian locations during 2018 (the last year for which complete irradiance data is available.)  Those locations are Alice Springs, Darwin, Rockhampton, Melbourne, and Cape Grim, and are shown on this map.

Fig.1:  Australian stations with solar exposure data

Cape Grim, set on a clifftop above the Southern Ocean, is most exposed to marine influences.  Melbourne, Rockhampton, and Darwin are surrounded by land but are subject to marine influence at times when the wind blows from the ocean.  Alice Springs has a desert climate and the ocean is thousands of kilometres away.  Most examples in this post will come from the Alice.

The Relationship Between Maxima and Minima:

Consider this plot of temperature at Walgett (NSW):

Fig. 2:  Latest weather graph for Walgett 27 – 31 January 2018

During a fine clear day the sun heats the ground which by conduction and convection raises the near-surface air temperature.  The hot ground emits upwelling IR, some of which greenhouse gases in the atmosphere absorb and re-emit in all directions, including towards the earth.  This is downwelling IR (DWIR), which adds to the solar radiation during the day, and slows the loss of heat at night.  The air temperature, and DWIR, peaks usually in the mid to late afternoon.  As the ground cools slowly throughout the evening and night hours, IR continues to be exchanged upwards and downwards, with enough being lost to space for ground and air temperatures to cool to the minimum.  This is usually reached, in fine clear conditions, sometime after sunrise.  And that is usually the time when DWIR also reaches minimum values.

Before I look at the relationship between DWIR and minima, let’s look at plots of maxima and minima.

Fig, 3:  Maxima and Minima at Alice Springs during 2018:

Note that usually (but not always!) peaks in maxima are matched by peaks in minima.  Here’s a closer look at the period from 6 May to 20 July, with minima scaled up by 19 degrees:

Fig. 4:  Maxima and Scaled Minima, 6 May – 20 July 2018

Note that maxima highs and lows precede those of minima by one day NEARLY ALWAYS.  (Sometimes they occur together, and sometimes maxima precedes minima by two days.)  The minimum temperature reflects the previous day’s maximum.  Why?  Due to DWIR, the ground cools slowly.  A hot day generates lots of DWIR, which keeps the ground (and air temperature) warmer next morning.  A cool day means less DWIR available next morning.  However, clouds lower maxima by reflecting sunlight but increase DWIR to keep nights and minima warmer, as we shall see later. The pattern seen above is also seen at Cape Grim, Melbourne, and Rockhampton, but not in Darwin where it is not so clear at all.

The Relationship Between Downwelling IR and Minima:

I used solar irradiance data to find daily (to 9.00 a.m.) minimum DWIR values for 2018 at Alice Springs, Darwin, Rockhampton, Melbourne, and Cape Grim, for comparison with daily temperature minima. 

Fig. 5:  Daily minima for 2018 at all stations

Fig. 6:  Daily minimum DWIR for 2018 at all stations

At all sites, as daily minimum IR increases, daily minimum temperature increases.  However, the strength of the relationship varies.  I calculated derivatives of Tmin and IR to find the daily change in values.  The relationship is strongest at Alice Springs, with a correlation of 0.69, Figure 5:

Fig. 7:  Change in temperature as a function of change in DWIR at Alice Springs.

Melbourne has almost exactly the same correlation (0.68), followed by Cape Grim (0.64) and Rockhampton at 0.61.  However Darwin is much different:

Fig. 8:  Change in temperature as a function of change in DWIR at Darwin.

The reason for this is not as complex as I thought, but first I’ll show a method of showing (and testing) the relationship between DWIR and Tmin more easily.

Converting DWIR to Representative Atmospheric Temperature

From the Bureau’s solar radiation glossary, http://reg.bom.gov.au/climate/austmaps/solar-radiation-glossary.shtml#globalexposure :

Downward infra-red irradianceis related to a `representative (or effective radiative) temperature’ of the Earth’s atmosphere by the Stefan-Boltzmann Law:

E = σ T4

Where: E = irradiance measured [W/m2]
σ = Stefan-Boltzmann constant [5.67 x 10-8 W/m2/K4
T = representative atmospheric temperature [K]

From this we can calculate the daily Representative Atmospheric Temperature (RAT) above each weather station.  Here is a plot of RAT for Alice Springs.

Fig. 9: Representative Atmospheric Temperature and Minima at Alice Springs

RAT is always colder than the surface.  Notice how closely Tmin tracks with RAT. 

To compare them more closely, I scaled up RAT by adding the average monthly difference from Tmin.  Now you can see how closely minimum temperature is related to RAT and thus DWIR.

Fig. 10:  Scaled Representative Atmospheric Temperature and Minima at Alice Springs

Zooming in to the period from 31 March to 4 June:

Fig. 11 :  Scaled RAT and Minima at Alice Springs, 31 March – 4 June 2018

The timing of variations is very close.

Here is a plot of the actual daily difference between minimum surface temperature and Representative Atmospheric Temperature.  I have marked some unusually low and high values for closer inspection..

Fig. 12:  Daily difference between Surface Minima and RATat Alice Springs

What causes these fluctuations?  Returning to actual temperature and calculated RAT, here is the plot for the year to 15 April:

Fig. 13:  RAT and Minima at Alice Springs, 1 January – 15 April 2018

Both Tmin and RAT usually move in unison, rising and falling together.  However, notice at point A there is very little difference between the values, but at point B there is a very large difference.

Here’s the plot for November and December.  A and B have very small differences, while C and D have very large differences.

Fig. 14:  RAT and Minima at Alice Springs, 6 November – 31 December 2018

Cloudy conditions increase downwelling IR.  With no daily cloud data, rainfall will be a proxy for some cloudy days.  (There will be plenty of cloudy days when there is no rain.)  Here is a plot of rainfall and the difference between surface minima and calculated RAT.

Fig. 15:  Rainy weather and Tmin minus RAT at Alice Springs

Rainfall appears to coincide with very low differences when RAT (derived from DWIR) has increased but corresponding Tmin has not increased as much as expected.  Let’s zoom in to look at Points A and B from Figure 13 above.

Fig. 16:  Rainy weather and Tmin minus RAT at Alice Springs, January – April

In fact rain coincides with nearly all of the low differences.  Point B remains anomalously high.  What about November and December?

Fig. 17:  Rainy weather and Tmin minus RAT at Alice Springs, November – December

Here we have a problem.  Points A and B from Figure 14 above line up with rain events.  Instead of being a low difference as expected, point C has a high value coinciding with a small rain event, and D is on its own.  Why?

When RAT is scaled up, the problem (and likely reason) is obvious:

Fig. 18  Scaled RAT and Minima at Alice Springs, December 2018

No IR data is recorded for 11 December.  I suspect that IR values should also be missing for 12 and 13 December.  Moving remaining data for the month two days later removes these strange inconsistencies (and also dramatically improves correlation between IR change and temperature change to above 0.7.)

Which still leaves the odd spike in Figure 13 at point B.

The Exception Proves The Rule

Here is a count of the number of days with no IR data at Alice Springs in 2018.

Fig.19:  Count of days with no data at Alice Springs

There are a few minutes of missing data on nearly every day, but data was completely absent for eight whole days in March, and three days in December.  Did the pyrgeometer stop recording suddenly?  Was it a sudden fault or was it failing gradually?  Figure 20 shows the 31 day centred running correlation between change in DWIR and change in Tmin, with missing days shown.

Fig. 20:  Centred 31 day running correlation between change in DWIR and change in Minima

If all is well, and the relationship between change in DWIR and temperature minima is sound, the correlation between them should be fairly constant.  However, if the pyrgeometer reads incorrectly (or else the temperature probe- another possibility, but not in this case), correlation will suffer.  This is shown in March and December.  From April to September, change in Tmin correlates well with change in DWIR being between 0.8 and 0.9 for nearly the whole time.

Now let’s look at Darwin, which we saw in Figure 8 above was poorly correlated.   The running correlation shows when faults may have occurred.

Fig. 21:  Centred 31 day running correlation between change in DWIR and change in Minima

The dips above coincide with equipment failure in January, March, November and December.  There also appears to be a problem in August – September.

It does not help that the equipment failures occur in rainy, cloudy periods (Wet and Build-up).

Fig. 22:  Rainy weather and Tmin minus RAT at Darwin

In the Dry, with no rain, the difference between Tmin and the RAT (Representative Atmospheric Temperature) still fluctuates wildly.  Here is a plot of the difference for June 2018:

Fig. 23:  Daily difference between Surface Minima and RATat Darwin June 2018

If the relationship is valid, and there are no recording problems, then large differences occur during fine and cloudless conditions and low values indicate cloudy conditions.  The daily total of Global Solar Exposure can also be a metric of cloudiness, because smaller amounts of sunlight reach the ground on cloudy days.   Figure 24 is a plot of the sum total of Global Irradiance in kiloWattminutes per square metre received each day.

Fig. 24: Daily total of Global Irradiance Darwin, June 2018

Apart from 10 – 12 June, the relationship holds.  Darwin’s apparent poor relationship between DWIR and Minima is very probably due to equipment failure.

The apparent exceptions to the “rule” that large differences between minima and Representative Atmospheric Temperature occur in dry, cloud free conditions, and small differences in cloudy conditions, in fact confirm it. 

Conclusion:

  • Downwelling infra-red radiation (so called “back radiation”) is real and measurable including at night.
  • It is greatly increased by cloud and humidity.
  • It results from daytime heating of the ground, which then loses heat by conduction, convection, evaporation, and radiation, into the atmosphere where the IR is repeatedly absorbed and re-emitted in all directions by greenhouse gases (including water vapour).
  • A warmer atmosphere from whatever cause, natural or enhanced, will result in greater downwelling IR.
  • Temperature Maxima highs and lows precede those of minima by one day NEARLY ALWAYS, due to the influence of downwelling IR.
  • Calculating Representative Atmospheric Temperature from downwelling IR using the  Stefan-Boltzman Law provides further insights.
  • The daily minimum RAT is always much colder than minimum temperature.
  • The difference between the two changes with the weather.  Sunny, dry, cloudless weather is associated with large differences, while cloudy weather is associated with small differences.
  • When recording error is accounted for there is very good correlation between downwelling infra-red irradiance and daily minimum temperatures at a range of sites across Australia.
  • In Australia, meteorological equipment can deteriorate for some time and fail completely, resulting in faulty data being included in national databases.
  • Finally, the effect of DWIR on minima is not site dependent.  Both Melbourne and Rockhampton have Urban Heat Island influence but the relationship is similar to that of other sites.  Minima are directly related to DWIR, but DWIR is increased not only by clouds, but also by large trees, nearby buildings, and areas of concrete and bitumen.

Homogenisation: A Test for Validity

September 8, 2014

This follows on from my last post where I showed a quick comparison of Rutherglen raw data and adjusted data, from 1951 to 1980, with the 17 stations listed by the Bureau as the ones they used for comparison when detecting discontinuities. 

Here is an alternate and relatively painless way to check the validity of the Bureau’s homogenisation methods at Rutherglen, based on their own discontinuity checks.  According to the “Manual” (CAWCR Technical Report No. 49), they performed pair-wise comparisons with each of the 17 neighbours to detect discontinuities.  An abbreviated version of this can be used for before and after comparisons.  For each of the 17 stations, I calculated annual anomalies from the 1961-1990 means for both Rutherglen and the comparison site, then subtracted the comparison data from Rutherglen’s.  I did the same with Rutherglen’s adjusted Acorn data.

A discontinuity is indicated by a sudden jump or drop in the output.  The ideal, if all sites were measuring accurately and there are no discontinuities, would be a steady line at zero: a zero value indicates temperatures are rising or falling at the same rate as neighbours.  In practice no two sites will ever have the same responses to weather and climate events, however, timing and sign should be the same.  Therefore pairwise differencing will indicate whether and when discontinuities should be investigated for possible adjustment.

Similarly, pairwise differencing is a valid test of the success of the homogenisation process.  Successful homogenisation will result in differences closer to zero, with zero trend in the differences over time.  The Bureau has told the media that adjustments are justified by discontinuities in 1966 and 1974.  Let’s see.

Fig. 1:  Rutherglen Raw minus each of 17 neighbours

pairwise diffs Rutherglen Raw

Note: there is a discernible drop in 1974, to 1977.  There is a very pronounced downwards spike in 1967 (ALL differences below zero, indicating Rutherglen data were definitely too low.)  There also a step up in the 1950s, and another spike upwards in 1920.  Rutherglen is also lower than most neighbours in the early 1930s.  Also note several difference lines are obviously much higher or lower than the others, needing further investigation, but the great majority cluster together.  Their differences from Rutherglen are fairly consistent, in the range +/- 1 degree Celsius.

Now let’s look at the differences AFTER homogenisation adjustments:

Fig. 2:  Rutherglen Acorn minus the neighbours: The Test

pairwise diffs Rutherglen Acorn

The contrast is obvious.  The 1920 and 1967 spikes remain.  Differences from adjusted data are NOT closer to zero, most of the differences before 1958 are now between 0 and -2 degrees Celsius, and there is now an apparent large and artificial discontinuity in the late 1950s.  This would indicate the need for Rutherglen Acorn data to be homogenised!

Compare the before and after average of the differences:

Fig. 3:

pairwise diffs Rutherglen Raw v Acorn average

There is now a large positive trend in the differences when the trend should be close to zero.

There are only two possible explanations for this:

(A)  The Bureau used a different set of comparison stations.  If so, the Bureau released false and misleading information. 

(B)   As this surely can’t be true, then if these 17 stations were the ones used, this is direct and clear evidence that the Bureau’s Percentile Matching algorithm for making homogenisation adjustments did not produce correct, successful, or useful results, and further, that no meaningful quality assurance occurred.

If homogenising did not work for Rutherglen minima, it may not have worked at the other 111 stations. 

While I am sure to be accused of “cherry picking”, this analysis is of 100% of the sites for which the identities of comparison stations have been released.  When the Bureau releases the lists of comparison stations for the other 111 sites we can continue the process.

A complete audit of the whole network is urgently needed.

The Australian Temperature Record- A Quick Update

August 23, 2014

This morning (Saturday 23 August) the Weekend Australian published articles by Graham Lloyd, their Environment Editor, on homogenisation practices at the Bureau of Meteorology as questioned by Jennifer Marohasy.  As I had a small part to play in bringing this to public light, here is a brief post to bring readers up to date.

The last paragraph in the second article reads:

“And the bureau says an extensive study has found homogeneity adjustments have little impact on national trends and changes in temperature extremes.”

This is laughable.  Here is a graph of the national means of Raw and Homogenised minima data from 83 sites (out of 104) that I was able to compare directly. (I also analysed the remaining sites, finding 47% bias, but because large slabs of data had to be left out this is not reliable.)

Fig. 1: Australian mean minimum temperature anomalies 1910-2012

Tmin comp

 The ‘raw’ trend is +0.63C per 100 years.  The adjusted trend is +1.05C.  The effect of the homogenisation adjustments is an increase in the national trend of +0.42C or 66.6%.  So much for “little impact.”

The article referred mainly to adjustments at Amberley and Rutherglen.

Fig. 2:  Amberley minima

amberley tmin

According to the BOM, the major adjustment was due to a pronounced discontinuity around 1980, that is, Amberley’s drop in temperature is not reflected in those of neighbouring sites, as is evidently correct.

Fig. 3:  Amberley compared with the mean of 5 Acorn neighbours

amb raw v reg mean raw

 However, the nearest Acorn site only 50km away, Brisbane Aero, also has a pronounced cooling trend, and a local cooling cannot be discounted.

An adjustment to the raw data before 1980 may be warranted, however, the size of the adjustment is questionable to say the least.  The resulting trend at Amberley has now become greater than the trend of adjusted data at every one of the Acorn neighbours, and more than +0.86C greater than their mean.

Fig. 4: Amberley’s adjusted (Acorn) data vs mean of adjusted data at 5 closest Acorn sitesamb acorn v reg acorn

Rutherglen in Victoria again shows cooling turned into warming.

Fig.5: Rutherglen minima

rutherglen tmin

And again, the Acorn adjustments make Rutherglen’s trend greater than every one of its neighbours’ adjusted trends, as well as their mean:

Fig. 6: Rutherglen Acorn vs neighbours’ Acorn (mean)

rutherglen acorn v reg acorn

 

The BOM is defending its territory, but this latest media exposure will mean increasing and critical scrutiny.

Click for further examples and background.

 

 

 

The Australian Temperature Record Revisited Part 2: Regional Effects

June 1, 2014

In my last post I showed how a numerical near-balance of adjustments to the ‘raw’ minimum temperatures at 83 out of 104 Acorn sites resulted in a 66.6% increase in warming trend across the nation.

I now turn to the effect on state and regional temperatures, which is enormously varied.

Figure 1 shows the official BOM trend map of trends in minima from 1910 to 2013:Trend map min

Note the little “bulls eyes” in various places, indicating where the local trend at individual sites is out of sync with the wider trend.  I’m sure you can identify Tibooburra in north western NSW, Richmond in northern inland Qld, Rutherglen in Victoria, Marree in northern SA, and Carnarvon on the WA coast.

Figure 2 shows the median position of all 104 sites, the four unequal area quadrants, and the number of sites I analysed in each with the increased warming resulting from adjustments.
Median network position map adj results

The concentration of Acorn sites in the south east of Australia, and the concentration of warming adjustment there as well, is plainly obvious.

Now I shall show each quadrant in turn, showing the trend difference at each site.

Figure 3:  South west Quadrant sites:
Bar graph SW Quad

Figure 4: SW Quadrant minimum temperature trends:SW quad chart

Figure 5:  North west Quadrant sites:Bar graph NW Quad

Figure 6:  NW Quadrant minimum temperature trends:NW quad chart

Figure 7:  North east Quadrant sites:Bar graph NE Quad

Figure 8:  NE Quadrant minimum temperature trends:NE quad chart

Figure  9:  South east Quadrant sites:Bar graph SE Quad

Figure  10: SE Quadrant minimum temperature trends:SE quad chart

In the next section I look at how the adjustments affect the mean minima in each state.  First I’ll look at the Northern Territory, which is atypical and based on only three sites (Alice Springs, Victoria River Downs, and Rabbit Flat), the two last with less than 50 years of observations.

Figure  11:  Northern Territory- cooling reversedNT Chart

Figure 12:  South Australia- adjustments result in less warmingSA chart

Figure 13:  Tasmania- adjustments result in less warmingTas chart

Figure 14: Western Australia- 23.7% increased warming.WA chart

Figure 15:  Queensland- 37% extra warmingQld chart

So far, every state has seen an increase in warming much less than the national mean of 66.6%, so much depends on the final two states.

Figure  16:  New South Wales- 245% extra warming!NSW chart

That is pretty amazing, but the result for Victoria is even more astounding.

Figure 17: VictoriaVic chart

The implications for the trend map in Figure 1 are obvious.  One hopes that those adjustments are well and truly justified!

In the next post I will discuss the remaining 21 sites which I am unable to compare directly, and later, the trend outliers.