Posts Tagged ‘rain’

Unprecedented South Australian Weather!

January 22, 2017

(and it has been like that for 178 years!)

There were more blackouts in South Australia a couple of days ago following a wild storm.  In a report in the Adelaide Advertiser, SA Power Networks spokesperson Paul Roberts is quoted:

“This is just another example of the unprecedented weather in the last six months,” Mr Roberts said, referring to bouts of wild weather that have hit power supplies hard this summer and the preceding spring.

21mm of rain was measured at the Kent Town gauge.

Just how “unprecedented” is Adelaide’s weather over the past few months?  I couldn’t find any records for the number of severe storms, so for a proxy I have made do with rainfall data from West Terrace and Kent Town in Adelaide.  The overlap period has very similar rainfall recordings so I joined the two series to give a record starting on 1 January 1839.  That’s 178 years of data.

When thinking about “unprecedented”, we need to check amount, intensity, and frequency.

Firstly, a few plots to give some context.  How unprecedented was Thursday’s storm?

Fig. 1: Rainfall for the first 21 days of January compared with Days 1 – 21 of every year

adelaide-rain-21-jan

Note Thursday’s rainfall had less rain than four previous occasions on this day alone, and 20 or so in previous Januarys.

Fig. 2: Rainfall for each day of 2016 compared with each day of every year:

adelaide-rain-2016

Note the December storm had extreme rain (for Adelaide) but not a record.

Amount and intensity has been higher in many previous years.  141.5mm was recorded on 7 February 1925.

Fig. 3: 7 day average rainfall over the years:

adelaide-rain-2016-7d-avg

The topmost dot shows the maximum 7 day average for each year.  2016 got to 13.4mm on 4 October- multiply by 7 to get the weekly total rain.  Note there were many wet and dry periods all through the record.

21mm of rain fell in a severe storm on Thursday, so I arbitrarily chose 20mm as my criterion for heavy rainfall in one day as a probable indicator of stormy weather.  I am the first to admit that 20mm might fall steadily all day and not be at all associated with wild winds, and wild winds can occur without any rain, but bear with me.

Fig. 4: Rain over 20mm throughout the year:

adelaide-rain-2016-above-20

There seems to be no increase in amount or intensity of rain at any time of the year.

Fig. 5: Frequency:

adelaide-rain-2016-cnt-above-20

Note 2016 had 7 days with above 20mm in 24 hours.  That’s the most since… 2000, when there were 8 days- and many previous years had 7 or 8 days, and 1889 had 9.  So no increase in frequency.

However, Mr Roberts was referring to the last six months, spring and summer.  So let’s look at rain events over 20mm from July to December, firstly amounts recorded:

Fig. 6: July to December Rain over 20mm:

adelaide-rain-above-20-last-6m

Nothing unusual about 2016.

Fig. 7:  Frequency of heavy rain July – December:

adelaide-rain-2016-cnt-above-20-last-6m

1973, 1978, and 1992 had the same or more days with over 20mm.

I now restrict the count to spring and summer only:

Fig. 8:  Spring and Summer frequency:

adelaide-rain-2016-cnt-above-20-last-4m

Not unprecedented: 1992 had one more.  Add in last Thursday’s event to make them equal.

Conclusion

Adelaide has a long climate record, showing daily rainfall has varied greatly over the years.  There is no recent increase in amount, intensity, or frequency for the whole year, or for the last six months or four months.  Spring and summer rainfall in 2016 was not unprecedented, and to the extent that spring and summer falls over 20mm are a proxy for storms, there is no evidence for an increase in wild weather.  This is normal.  Get used to it, Mr Roberts, and make sure the electricity network can cope.

 

Land and Sea Temperature: South West Australia

November 29, 2016

This year, the south-west of Western Australia has recorded some unexpectedly low temperatures.  Has this been due to rainfall, cloud, winds, or the cooler than normal Leeuwin Current and Sea Surface Temperatures in the South West Region?

In this post I examine maximum temperature and rainfall data for Winter in South-Western Australia, and Sea Surface Temperature data for the South West Region, all straight from the Bureau of Meteorology’s Climate Change time series page .

All temperature data are in degrees Celsius anomalies from the 1961-90 average.

Figure 1 is a map showing the various Sea Surface Temperature monitoring regions around Australia.

Fig. 1

sst-regions

The Southwest Region is just to the west and southwest of the Southwest climate region, and winter south westerlies impact this part of the continent first.  2016’s winter has seen maxima drop sharply.  In fact, it was the coldest winter since 1993:

Fig. 2:  Southwestern Australia Winter TMax Anomalies

sw-tmax

There is a relationship between rainfall and Tmax- as rain goes up, Tmax goes down, so here south west rainfall is inverted and scaled down by 100:

Fig. 3:  TMax and Rain:

sw-tmax-rain

The next plot shows TMax and the South West Region’s Sea Surface Temperature anomalies (SST):

Fig. 4:  TMax & SST:

sw-tmax-sst

Again, related: both have strong warming from the 1970s.  Next I check for whether there was a real change in direction in the 1970s, and if so, when.  To do this I use CuSums.

Fig. 5:  CuSums of Winter TMax and SST compared:

sw-tmax-sst-cusums

Both have a distinct change point: 1975, with SST warming since, but TMax appears to have a step up, with another change point at 1993 with strong warming since.  Rainfall however shows a different picture:

Fig. 6:  CuSums of Winter Rainfall

sw-rain-cusums

Note the major change at 1968 (a step down: see Figure 3), another at 1975 with increasing rain to the next change point at 2000, after which rain rapidly decreases.

I now plot TMax against rainfall and SST to see which has the greater influence.  First, Rain:

Fig. 7:  TMax vs Rain:

sw-tmax-vs-rain

100mm more rain is associated with about 0.5C lower TMax, but R-squared is only 0.22.

Fig. 8:  TMax vs SST:

sw-tmax-vs-sst

A one degree increase in SST is associated with more than 1.1C increase in TMax, and R-squared is above 0.51- a much closer fit, but still little better than fifty-fifty.

TMax is affected by rain, but more by SSTs.

I now look at data since the major change points in the 1975 winter.  The next three figures show trends in SST, Rain, and TMax.

Fig. 9:  Trends in SST:

sw-sst-trends

Warming since 1975 of +1.48C/ 100 years.

Fig. 10:  Trends in Rainfall:

sw-rain-trends

Decreasing since 1975 at 89mm per 100 years (and much more from 2000).

Fig. 11:  Trends in TMax:

sw-tmax-trends

Warming since 1975 at +2.14C per 100 years.

Detrending the data allows us to see where any of the winters “bucks the trend”.  In the following plots, the line at zero represents the trend as shown above.

Fig. 12:  SST Detrended:

sw-sst-detrended-75-to-16

Fig. 13:  Rainfall Detrended:

sw-rain-detrended-75-to-16

Fig. 14:  TMax Detrended:

sw-tmax-detrended-75-to-16

Note that SST in 2016 is just below trend, but still above the 1961-90 average.  Rainfall is only slightly above trend, and still below average.  However TMax is well below trend, and well below average, showing the greatest 12 month drop in temperatures of any winter since 1975.

My conclusions (and you are welcome to comment, dispute, and suggest your own):

  • Maximum temperatures in winter in Southwestern Australia are affected by rainfall, but to a much larger extent by Sea Surface Temperature of the South West Region.
  • The large decrease in winter temperature this year cannot be explained by rainfall or sea surface temperature.  Cloudiness may be a factor, but no 2016 data are publicly available.  Stronger winds blowing from further south may be responsible.

DTR, Cloud, and Rainfall

September 19, 2016

In my last brief post I showed how Diurnal Temperature Range is related to rainfall in Northern and Southern Australia in Northern and Southern wet seasons (which correspond roughly to summer and winter).

In this post I show the relationship between DTR and daytime cloud, and between rainfall and daytime cloud, and something very peculiar about South-Western Australia.

All data are taken straight from the Bureau’s Climate Change Time Series page.

DTR is affected by rainfall through Tmax being cooled by cloud albedo, evaporation and transpiration, and Tmin warmed by night cloud and humidity.  There must be a relationship between clouds and rain, although it is (rarely) possible to have rain falling from a clear sky with no visible cloud.  Rain is easily measured in standard rain gauges.  Cloud is calculated by trained observers, and we only have data for 9 a.m., 3 p.m., and daytime cloud.  The data give no indication of cloud type, thickness, or altitude, just amount of sky covered (in oktas, or eighths).

Here I show scatterplots for Australia as a whole annually, and for Northern, South-Eastern, and South-Western Australia in summer and winter.  I calculate both rainfall and cloud as percentage differences from their means.

Fig. 1:  DTR vs Rain for Australia annually:

dtr-vs-rain-oz-ann

Fig. 2:  DTR vs Cloud for Australia annually:

dtr-vs-cloud-oz-ann

Notice much better correlation between DTR and Cloud.

Now let’s look at the relationship between rainfall and daytime cloud.

Fig. 3:  Percentage difference in Rainfall vs percentage difference in Cloud for Australia annually:

rain-v-cloud-oz-ann

Note a 10% increase in cloud cover could be expected to be associated with a 25% increase in rainfall.

Fig. 4: Percentage difference in Rainfall vs percentage difference in Cloud North Australian summers:

rain-v-cloud-n-oz-summ

Fig. 5: Percentage difference in Rainfall vs percentage difference in Cloud North Australian winters:

Note how rainfall in the North Australian dry season varies proportionally more, but has a slightly lower correlation (>0.8 vs 0.9).

Fig. 6: Percentage difference in Rainfall vs percentage difference in Cloud South-East Australian summers:

rain-v-cloud-se-oz-summ

Note the much greater effect of cloud on rainfall in the southern dry season.

Fig. 7: Percentage difference in Rainfall vs percentage difference in Cloud South-East Australian winters:

rain-v-cloud-se-oz-wint

Now, get ready for a surprise.

Fig. 8: Percentage difference in Rainfall vs percentage difference in Cloud South-West Australian summers:

rain-v-cloud-sw-oz-summ

Fig. 9: Percentage difference in Rainfall vs percentage difference in Cloud South-West Australian winters:

rain-v-cloud-sw-oz-wint

What’s going on in the south-west?

Here’s how DTR compares:

Fig. 10:  DTR vs percentage difference in rainfall: South-west Australia

dtr-vs-rain-sw-oz-ann

Similar relationship to everywhere else.

Fig. 11:  DTR vs percentage difference in cloud cover: South-west Australia

dtr-vs-cloud-sw-oz-ann

And this graph clearly shows the relationship between rain and cloud is closer in the wet seasons, but also clearly shows that South-west Australia is an extreme outlier.

Fig. 12:  R-squared comparison between rain and cloud in wet and dry seasons

chart-seasonal-r2

Why the huge difference?  There is no relationship between cloud and rain in south-west Australia, unlike everywhere else.  The South-West has seen a marked decline in rainfall since the late 1960s, but an increase in cloud cover.  It seems counter intuitive, but there you go.

Any suggestions are welcome.

DTR and Rainfall

September 12, 2016

I’ve been looking at DTR and rainfall relationships for Northern and Southern Australia.  I’ve also analysed them by winter and summer (southern and northern wet seasons).

I’ve used a different approach.  Instead of comparing DTR with rainfall anomalies (differences from the mean) I’ve converted these to percentage differences from the mean rainfall.

Data are from the BOM climate change page, so DTR is based on Acorn.  DTR before 1950, and especially before 1932, may be suspect.  However the data are useful for this comparison.

Propositions to test:

DTR which is supposed to decrease as a fingerprint of greenhouse warming, is strongly related to rainfall variation.

There is an unexplained increase in DTR around 2001.

In the time series plots below, rainfall has been inverted, so ‘up’ is dry and ‘down’ is wet.  The rainfall anomalies are expressed as percentages difference from the mean and scaled down by 50.

dtr-rain-oz-ann

dtr-vs-rain-oz-ann

dtr-rain-n-oz-ann

dtr-vs-rain-n-oz-ann

dtr-rain-s-oz-ann

dtr-vs-rain-s-oz-ann

Now comparisons during northern wet season (November to April, basically summer), and southern wet season (May to October- winter and spring).

dtr-rain-oz-summ

dtr-vs-rain-oz-summ

dtr-rain-oz-wint

dtr-vs-rain-oz-wint

dtr-rain-n-oz-summ

dtr-vs-rain-n-oz-summ

dtr-rain-n-oz-wint

dtr-vs-rain-n-oz-wint

dtr-rain-s-oz-summ

dtr-vs-rain-s-oz-summ

dtr-rain-s-oz-wint

dtr-vs-rain-s-oz-wint

Results:

table

 

 

 

 

 

 

Notice that Southern Australian winters dominate DTR.  The impact of rainfall on DTR in Southern Australian winters is twice that in Northern Australian winters, and correlates better as well.  Also note that Southern summers have very slightly higher DTR change per rainfall change and slightly better correlation than Northern.  No doubt you realise winters up here can’t really be compared with southern winters, being mild and very dry.  In many places it is not very difficult to double the mean rainfall in winter with not many millimetres of rain, and zero rain for many months in winter is not unusual.

This plot shows Cusums of DTR and inverted, scaled rainfall.

cusums

The turning points line up exactly, including 2001.  There is no visible unusual change in 2001.  There are however times when the Cusums diverge: 1932, 1958, 1985, and 2003 and 2011.

DTR is strongly related to rainfall variation, especially in southern Australia in winter.

There is no unexplained increase in DTR in 2001.

 

The Disconnect Between Theory and Reality- Part 2: Winters vs Summers

February 12, 2016

UPDATE:  PLEASE NOTE UAH DATA FOR THIS POST ARE FROM 6.0 BETA 4.  BETA 5 WILL GIVE DIFFERENT RESULTS.

It was two years ago in 2013 that I last posted on the difference between climate scientists’ expectations and reality, so in this series of posts I bring these points up to date, and add a couple of related points.

What the climate scientists tell us:

Dr Karl Braganza in The Conversation on 14/06/2011 lists the “fingerprints” of climate change (my bold).

These fingerprints show the entire climate system has changed in ways that are consistent with increasing greenhouse gases and an enhanced greenhouse effect. They also show that recent, long term changes are inconsistent with a range of natural causes…..
…Patterns of temperature change that are uniquely associated with the enhanced greenhouse effect, and which have been observed in the real world include:
• greater warming in polar regions than tropical regions
• greater warming over the continents than the oceans
• greater warming of night time temperatures than daytime temperatures
greater warming in winter compared with summer
• a pattern of cooling in the high atmosphere (stratosphere) with simultaneous warming in the lower atmosphere (tropopause).

And later

Similarly, greater global warming at night and during winter is more typical of increased greenhouse gases, rather than an increase in solar radiation.

In this post I look at whether there is a pattern of greater warming in winter than summer.

This indicator appears to be FALSIFIED for both Northern and Southern Hemispheres:

Fig. 1:  Winter vs Summer, Northern Hemisphere (UAH)

summ win NH

Fig. 2:  Winter vs Summer, Southern Hemisphere (UAH)

summ win SH

And at the Poles:

Fig. 3: Winter vs Summer, Northern Polar region (UAH)

summ win NP

Summers warming faster than winters.  And in Antarctica:

Fig. 4: Winter vs Summer, Southern Polar region (UAH)

summ win SP

Winters (which are mostly night) are cooling much faster than summers.

In Australia overall however, winters are warming faster than summers.

Fig. 5: Winter vs Summer, Australia (UAH 1979-2015):

summ win Oz uah

And Acorn surface data since 1979:

Fig. 6: Winter vs Summer, Australia (Acorn 1979-2015):

summ win Oz acorn 7915

And since 1911:

Fig. 7: Winter vs Summer, Australia (Acorn 1911-2015):

summ win Oz acorn 19112015

However, the patterns are very different in different Australian regions.  North Australia has winters warming faster than summers:

Fig. 8: Winter vs Summer, Northern Australia (Acorn 1911-2015):

summ win Oz nth

While Southern Australia has exactly the reverse:

Fig. 9: Winter vs Summer, Southern Australia (Acorn 1911-2015):

summ win Oz sth

Let’s look at different parts of the South, first the South East:

Fig. 10: Winter vs Summer, South Eastern Australia (Acorn 1911-2015):

summ win Oz SE

And the South West:

Fig. 11: Winter vs Summer, South Western Australia (Acorn 1911-2015):

summ win Oz SW

This shows a particularly strong summer warming effect.

In the North, the pattern seems driven by greater summer rainfall and drier winters:

Fig. 12:  Summer and Winter rainfall anomalies, Northern Australia

summ win Oz rain Nth

There has been much less winter rain in the Southwest (in the Southeast, there has not been as much variation):

Fig. 13:  Summer and Winter rainfall anomalies, South Western Australia

summ win Oz rain SW

In both the North and Southwest, there are distinct changes in rainfall in the late 1960s or early 1970s:

Fig. 14:  Northern Summer rainfall changes

summ rain Nth

Note the long term slow decrease to 1973, the wet 1970s and dry 1980s, and all except 6 wetter than average seasons since 1991.

By contrast, the South Western rainy season shows a long term slow increase with great variability until the 1960s, with a sharp step down in 1969, and another in 2001, with less year to year variability.

Fig. 15:  South Western Winter rainfall changes

winter rain SW

This shows up in trend maps of summer and winter rainfall 1970-2014:

Fig. 16:  Trends in summer rainfall

summ rain 19702014

Fig. 17:  Trends in winter rainfall

winter rain 19702014

The effect of less winter rain on temperatures in the following summer in South Western Australia is clearly seen in this scatterplot:

Fig. 18:  Summer means and previous winter rain:

summ T vs win rain SW

While the IPCC and its acolytes in the Climate Council predict less rainfall for southeastern and southwestern Australia, this would not be difficult given the trend for southwestern Australia had been established for 20 years before the IPCC was even formed, and 45 years before AR5. Northern Australian rainfall is not mentioned.

Assessment of this evidence for the enhanced greenhouse effect: FAIL.  Tropospheric data show this to be falsified in both Hemispheres and both Poles.  Australia appears to go against this pattern, but drastic changes in rainfall patterns in the Northwest and Southwest appear to be involved in the difference between north and south.

Theory has been mugged by reality yet again.

Rain and Surface Temperature Part 3

December 2, 2015

I have recently shown how the difference between surface maxima for Northern Australia and Temperature of the Lower Troposphere (TLT) for Australia as a whole is very largely due to rainfall variation in the Northern Australian region alone.
Fig. 1:  Northern Australian rainfall compared with the difference between North Australian surface maxima and Australian TLT (120 month means)

Nth rain v nth diff 120m
Now I turn to comparison with another region: that of Tropical Land.  All but six degrees of Latitude of the Northern Australian region is in the tropics, so most of it will be covered by TLT for Tropical Land. How much influence does Northern Australian rainfall have on the difference between Northern Australian surface maxima and TLT for all land in the tropics around the globe?
Fig. 2: Northern Australian rainfall compared with the difference between North Australian surface maxima and Global Tropical Land TLT (12 month means)

Nth rain v tropic land diff 12m

Fig. 3: Northern Australian rainfall compared with the difference between North Australian surface maxima and Global Tropical Land TLT (decadal means)

Nth rain v tropic land diff 120m

Considering that the Tropical Land TLT measures temperature above large tracts of Africa, South Asia, and Central and South America as well as tropical Australia, this result is amazing: on a decadal timescale, Northern Australian rainfall variation alone accounts for the same proportion of the surface- tropospheric difference of northern Australia surface maxima- Australia TLT as northern Australia surface maxima- tropical land TLT.

Surface temperatures cannot be understood separately from rainfall, and especially tropical rainfall. We can also conclude that as the decadal comparison of North Australian rain and surface-atmospheric differences have similar results for both Australia and Tropic Land datasets, UAH Version 6.0 represents TLT in various regions very well. Further, if the rest of the world’s tropical land areas behave as Australia does, then the world’s climate is dominated by tropical rainfall.

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.

Weather predictions- final check

January 3, 2014

This is the final post in my series of checking a hunch that temperature change indicates a weather change 160 days later.

Back on November 3, I predicted:

“December
2 to 10 unstable; 12-13-14; 16-17-18-19; 22-23-24; 26 to 31 unstable.”

I did not change this in December.

This is how  I went:

Dec2013 predictions check

I’ve marked with green bars the predicted dates of unsettled weather as above.  Red bars show the actual times.  They match.

And finally, here’s my graph showing predicted weather events for April to June.  Again, green bars indicate dates when weather events may be expected.Apr-June 2014

I will leave this topic for now, not because the method doesn’t work (it does!), but to concentrate on other interests.

Weather predictions: December

December 1, 2013

At the start of November, I said:

“November
5 to 10 unstable; 13 to 21 unstable with several events; 26-27-28-29-30 unstable.”

All correct, 1 miss.  Instability with some very wild storms marked much of November especially in the South-East of the state.

Now I suppose anyone could have predicted storms for November.  But remember, back in August I had said:

“November

5-6-7, 9-10, 13-14-15, 17-18-19-20, 27-28-29.”

Here’s a chart showing August predictions in light green and early November predictions in dark green.octdec13resultsnov

5 right, I miss.  I should have stuck with my original predictions!

So the method is holding.

Predictions for December to 31 March remain the same as I predicted last month.  As well, I expect weather events around these dates in April and May (+/- 1 day):

2,4,7,11,15,20,23,25, May 1, 8,11.

April should have unstable weather, and I would not be surprised if we get significant rain.

 

Weather Predictions November – March

November 3, 2013

At the start of October, I made the following predictions:

October
Sep 30- 1-2, 8-9-10, 14-15-16, 20-21-22-23, 25?, 28-29-30-31-Nov 1 (possibly 2 events).

Changes were detected on:- 2-3, 8-9, 13-14, 17-18, 22-23- 24, 28- 29-30-31 (the”possibly 2 events” was true indeed with a series of storm fronts that persisted.)Oct 13 results & pred

Dark red is Tmin, pink is Tmax, black is inverted pressure, blue is rain, green is the 160 day lagged Tmin, yellow is 40 day lagged pressure.  I may try 40 day lagged temperature in future.   November and December predictions are shown as red ovals.  So there were 5 correct predictions, 1 wrong, and 1 miss (although the wrong event would have been a “correct” if it had been a day earlier).  Not bad for 160 days out, I console myself.  I also suspect there has been a slight acceleration of weather systems, so perhaps I should be looking at events around 158 days ahead instead of 160.  No matter for now but I will keep an eye on it.

Predictions for November and December (slightly adjusted from last month):

November
5 to 10 unstable; 13 to 21 unstable with several events; 26-27-28-29-30 unstable.
December
2 to 10 unstable; 12-13-14; 16-17-18-19; 22-23-24; 26 to 31 unstable.

 January to March:

Here is the graph I use for predicting weather changes, which shows 2nd derivative, or acceleration/deceleration, of minimum temperature.

jan-mar 2014 predictions

Think of a cool change moving through from the west.  A large temperature differential, or a fast moving change, both have the same effect.  When the green line goes below zero, the temperature has decelerated, and a change (or unsettled weather) occurs in the time period indicated.  Several rapid oscillations appear to relate to unsettled weather.  My only problem is I am averaging over a very wide region, and the time lag may be changing.

There is always something to learn.