Re: whether a warm summer follows a warm winter, and such questions.
I have ten years data, beginning Autumn 1999, for 40 mean seasonal temperatures at Manilla. I found the 10-year mean for each season (Autumn, Winter, Spring, Summer) and subtracted it to get the anomaly for each of the 40 seasons. I smoothed the results (1:2:1)/4. A graph shows the following:

Seasonal temperature anomalies run from +1.05 degrees (Spring '02: the hottest spring) to -1.14 degrees (Summer '07'08: the coldest summer).

The curve is sinusoidal, with a period of about 1.6 years.

Peaks on the curve are:
Winter '99: +0.06
Spring '00: +0.25
Spring '02: +1.05
Autumn '04: +0.26
Spring '05: +0.61
Summer '06'07: +0.84
Spring '08: -0.01

Troughs on the curve are:
Summer '99: -0.61
Spring '01: -0.74
Winter '03: -0.37
Spring '04: -0.35
Winter '06: -0.22
Summer '07'08: -1.14

With 13 peaks and troughs, one might expect about 3 to occur in each season, but Spring has twice as many: 4 peaks and two troughs. Autumn has just the one peak.

There are two major sustained events: one warming and one cooling.
1. From Spring '01 to Spring '02 the anomaly rose by 1.79 degrees at the rate of 0.45 degrees per season.
2. From Summer '06'07 to Summer '07'08 the anomaly fell by 1.98 degrees at the rate of 0.5. degrees per season.

Do any of these observations ring any bells for anyone?

In case you are interested, the linear trendline has no slope at all.

A picture is worth a thousand words. (I just made that up .)

I think there is no doubt that the temperature anomalies form a sine wave.
The period of the wave is a good deal longer than a year.

In the graph of the above post, each data point refers to a season. The four points in each year are Autumn, Winter, Spring, and Summer. Summer begins in December of a given year and extends into the next year.
On the smoothed curve, winter anomalies predict summer anomalies to some extent in half the years: 2000, 2001, 2002, 2005 and 2008. In each of these years except 2001 there is a peak anomaly in spring with winter and summer on the flanks of it. In 2001 spring marks a trough,not a peak.
In the other five years winter and summer anomalies are dissimilar, especially in 2006 and 2007.
Five successful forecasts out of ten is not a very good score. :rolleyes:
Perhaps data from other places might show a better success rate? :evillaugh:

I just noticed the strange repeated pattern of the peaks:
The first peak is low, the second peak is higher and broader with an up-slope on the crest, and the third peak is very high and sharp. Then the pattern repeats.
Any bets on whether it will repeat again? :nerd:

:wave: Hi, guys! :wave:
I would really appreciate comment on this graph.
Does the pattern relate to any data or model already discussed?
Is this the pattern of warm and cool seasons that you had at your place?

I now have data showing that autumn and (probably) winter this year have mean temps close to normal. That makes the current peak wider than the other two low peaks: winter '99 and spring '04.
All the same, the 1.6 year cycle is so strong, the graph suggests (to me) that the next few seasons are very likely to be cool.

Thanks.

Excellent topic Surly..not sure that too many people have data.

I'll put my 30+ years of temperature data through some analysis and see what comes out. I've been distracted with trying to get a webpage to work properly. This would be a very welcome excuse to leave HTML etc behind!

There is a strong El-Nino signature in that graph for the warmest periods (Spring in 2002 & 2006). Not surprising really.

The current mini-ElNino might be waning already, so a dip in temps may occur shortly although that could depend on where the warm water currently in the Pacific Ocean Tropics flows.

Surly B

You can use your model to predict the future temps / trends at your site. But you have to wait years before you will find out if it has merit.

But you can also hindcast...

I have replicated your graph using Tamworth BoM data, and overlayed it over your graph:



Close - similar trends are evident.

Now, you can use the longer Tamworth dataset to see if what you have identifed holds in the past:



Hints of the identified pattern, but you have to say that it's not really there.

It's late - I'll post up my methodology and links tomorrow night if I get the chance.

cheers

Thanks for a great follow-through, Arnost.
I tried to count peaks and troughs (notoriously difficult ) on the 66(?) year Tamworth record that you have graphed.
I get 41 peaks and 38 troughs. That is a mean period for the cycle of 1.65 years. Is this a "well-known fact" that I haven't heard of?
I notice that there are a few peaks on the longer record that are a good deal warmer than those on my graph. I can't resolve them, but they seem to be in 1946, 1966 and 1981. The coldest time was about 1956.
I guess the major peaks and troughs will show up on annual figures as well. But to speak of a "hot year" clearly tells little of the story.

Just a thought: A cycle of 1.666... years would get into step with the seasonal cycle every seven years. If summer is a peak in one year, a summer peak might be expected seven years later.
My maths OK? :rolleyes:

My maths OK?
No. A 5-yr cycle.

Interesting that the temp anomaly has been flat for the last 65years with variations up and down only.

I'm probably getting thicker in the head with time..when I put a 1:2:1 filter on my data the result is virtually the same as the 3 month (seasonal) data.

I don't know where I may have gone wrong in the calculation.

Keith, are you using monthly data, perhaps? :nerd:

Re: the 5/3-year cycle getting in step with the seasons every five years:
I think I can see a 5-year cycle in Arnost's longer-term graph.
That would be expected only if one particular season were more liable to anomalies than the others. From my (very small) data set, I suspect that may be true of spring (see OP). If so, an enhanced peak or trough might occur when a 5/3-year peak or trough fell in spring.

This is clearly a case for spectral analysis. The only time I used that I had a famous statistician holding my hand. The programs are unbelievably fast now (used to take all night), but the traps for the unwary are still there. :evillaugh:

What climate forcing mechanism has a period of 5/3 years?

roves,
"Global warming" is almost certainly present in the Tamworth data set, but it is very hard to see on this graph. The trend line would rise only one degree in the width of the graph.
I intend to post later on a kind of graph that shows it better .

Yes, to start with, then recalculated to seasonal.

Sorry for dumb questions but can you please briefly explain how you set up your calculations?

OK – here’s how I did it, so if anyone wants they can replicate…


The Tamworth temperatures can be obtained from:

http://www.bom.gov.au/climate/data/weather-data.shtml

where you can select various meteorological data.

Select Temperature, and then in the new box download both the monthly Maximum Mean and the Minimum Mean (the monthly mean is not available). The Tamworth station IDs are Tamworth AP (station 055054) and Tamworth AP AWS (station 055325).

Create a monthly mean from the above data by adding the maximum and minimum mean temps for each month and divide by two. I cross checked this against the data at GISS:

http://data.giss.nasa.gov/work/gistemp/STATIONS//tmp.501957620000.1.1/station.txt

which has what looks like the full Tamworth AP dataset. Note that the Tamworth AP data goes back to 1907 (as does the GISS data set) but there are a few gaps / missing months. Here is the difference between my mean and GISS:



Though there are a couple of downspikes, the vast majority is either the same or off by 0.05C attributable to rounding.

I then spliced the two datasets (the Tamworth AP and the Tamworth AP AWS) together without any adjustments. I guess that the temps should be identical as the AWS is basically at the same location as the screen that it replaces. There is a period of overlap and this is the difference.



The Airport AWS seems to run a bit cooler in the winter, and while purists may disagree as far as I’m concerned it’s negligible for this exercise.

The next step was to create a base from which an anomaly could be calculated. I decided to use the 1961-1990 period. (Note, if I used the same 10 year period as a base that Surly B used - the match may have been better…). To calculate the anomaly, I added every individual months means (i.e. added all the Jan’s, all the Feb’s etc) and divided by 30.

I then subtracted this monthly value from all the respective months to get the anomaly.

I then added the three months in each season (i.e. Mar, Apr & May for Autumn etc) and divided by three. This is the main (red) dataset in my graphs in my previous post.

I then did the 1:2:1 smoothing as follows (I think this is where you may have gone wrong Keith). The first value (Winter 1942) was:
(Autumn 1942 X 1 + Winter 1942 X 2 + Spring 1942 X 1) / 4,

and the next (Spring 1942) was:
(Winter 1942 X 1 + Spring 1942 X 2 + Summer 1943 X 1) / 4

This is the smoothed (blue) dataset in my graphs in my previous post.

And that’s it! If you have any questions let me know. I hope that this was usefull.

My calculations are essentially the same as Arnost's, but I started from my own daily max and min temps at Manilla. That is where my interest lies.
I had already summarised my data by month and by season before I thought of doing this analysis. I do monthly and season reports for the local paper.
One could use monthly data directly with a wider smoothing window (half-width 8 months) for a similar result.
When I drew the graph, I used the whole 10 years of data as my base period. I intend to keep using that base period for the time being. Data for future seasons may well trend warmer or cooler than the chosen base.
I am glad Arnost stated his choice of base period, which saves me asking.

BTW Arnost, I believe the new Tamworth AWS is some kilometres from the old airport site.
The AWS is a "blasted heath" (without the witches).

Maybe that explains why the winter months are almost a degree C cooler at the AWS... which is not really negligible.

>>>OK – here’s how I did it, so if anyone wants they can replicate…<<<.... says Arnost.

I am really keen to see some others replicating.

How big a region does the Manilla and Tamworth data represent? Certainly the NW Slopes, but how about New England? The North Coast? Brisbane? Even Sydney?
Melboune must be different, as they have had a drought. :evillaugh:

I trust I won't see three old hags near the AWS chanting magic words about global warming (and no, I'm not a Macbeth, though I think some scientists practise rhabdomancy).

Thanks Arnost for the calculation notes. I did indeed get that part wrong (in how I understood the process, not the calculation in principle), so I'll try again.

rhabdomancy... well you learn something every day!

This is a plot of my data from 1999 to 2008:



The traces are self-explanatory. The anomalies were calculated with reference to the average for 1970-2008, the longest period of complete years for which data aren't missing. The period up to 1978 from which I calculated the anomalies, actually covers the former official BOM station about 3 km southwest of here but for all intents and purposes the maxima would have been the same here (minima were much lower as the elevation was 30 metres or so lower).

There appears to be a cycle of about 1.75 years.

Just for the heck of it more or less, I plotted this chart of my rainfall data:



The same criteria apply to data pre-1978 as with the temperature plot previously posted, with the added proviso that the averages are taken from 1950, the first year of record.

Again there appear to be cycles, but rather more variable than with the temperatures.

Good stuff, Keith! :cheers:

I hadn't finished, but the program decided I had.

Yes, I count six peaks and six troughs on your 10-year temp anom graph. That would be 5/3 year cycle.
BTW, your 1:2:1 smoothing trace is lagged behind the raw trace. I think the weighting should be centred on the raw data point. That would agree with what Arnost said.
Our data seem to agree on the highest peak and lowest trough. My highest peak is spring 2002. I am not sure we have used the same convention for x-axis labels. Your high peak (raw) is three grid lines after the one labelled "2002". Which season would that be?
My lowest trough is summer 2007-8. Yours is on the "2008" grid line.

"Lucia" has designed a mug that has the 5/3-year cycle on it, but I don't know what she is quoting. I can't read the legend:
http://www.cafepress.com.au/luciablackboard

The Indian Ocean Dipole Dipole Mode Index seems to have this cycle:
http://www.jamstec.go.jp/frcgc/research/d1/iod/HTML/Dipole%20Mode%20Index.html
I am surprised ROM didn't mention it.
:wave:

Keith, I intend to try the spectral analysis to substantiate the cycle. I see that Excel Add-Ins includes Fast Fourier Transform. With Excel Help and various blogs I should be able to manage it.

I reckon the 5/3-year cycle should be called the "Surly Cycle". :nerd:
However, if phenomena such as the IOD show it, surely there must be a cacophony of discussion of it out there by now.

here is an odd one for you-

at spencers creek
http://www.snowyhydro.com.au/snowDepth.asp?pageID=46&parentID=6&mode=submitted

practically every even year has higher snow falls than the odd years.

mobihci
Thanks for that. The internet seems full of such calculators. Unfortunately for me, the question that they answer is never the one I am asking.
I thought of laboriously copying the series of peak winter values into a list to look at the pattern. Then I realised that Harry Nyquist's folding frequency was in the way. Using annual data points, only cycles longer than two years can be resolved.

Keith, I just noticed that your graph shows maximum temperature, not mean temperature. I also have graphs of maxima and minima, which are not quite ready. Maxima and minima graphs differ quite a bit.

Keith, For my plot of smoothed data, I used the option in Excel Charts to format the "line" as "Smoothed". I think this is a logical thing to do, as we are trying to make the trace less jumpy. I believe one can also legitimately use the smooth line to estimate whether a peak is nearer to one season or the next, or just half-way between.
Perhaps you used a different program for the rainfall: the lines are smooth.

I compared the major peaks and troughs on the Dipole Mode Index with those on the Tamworth temperature plot that Arnost posted.
If there is a relation it is cryptic. Major peaks and troughs on one coincide with quiet times on the other.

Hi Surly,

Seems i was suffering dyslexia in regard to what you had plotted; I was trying to just get things going and must have overshot the platform. Anyway it at least had something to show.

Best of British with Excel's Fourier analysis..I've used it before but it's a bit of a dinosaur. One thing that's needed is exact multiples of 2^2 data (eg 4,16,32,64,128,256 etc)...though I understand most Fourier stuff requires that. There are some free downloads around the traps (not necessarily Excel, though).

I have separate signal processing software which will also generate wavelets..might try and post something with that. I struggle a bit with the outputs of some of the routines.

My charts were both done in Excel 97; I forgot to smooth the lines in the first graph. I did wonder why one trace was behind the other; although the calculation is centred on the middle value it's likely due to the way I've laid out the final data in the sheet (using pivot tables).

I've used plain years to describe the X-axis to avoid cluttering the legend, but eg 2008 is summer 2007-08 and lines in between each year are autumn, winter and spring in that order. I probably could have been a bit more inventive but wasn't feeling like it.

OK, here we go again (having trouble with sizing images for some reason but I'm very tired and don't want to fiddle further with them):





The top image is a recast of the earlier graph to bring the data into correct alignment with each other.

The other chart is the 1:2:1 seasonally filtered data plotted against a wavelet reconstruction of the raw seasonal data with cycles <2 years removed.

In NSW the North West Slopes and the Sydney Basin have had very similar seasonal temperature anomalies in the last 10 years.
So far, I have simply plotted Keith's Kings Langley figures for mean maxima on my graph for Manilla. The absolute anomaly values differ, but the peaks and troughs mostly occur at the same time in both places, and the trends up and down generally match.
This is information that I don't think has ever been documented before.
Thanks very much, Keith! :cheers:

A picture is worth a thousand words. (I just made that up .)


I think there is no doubt that the temperature anomalies form a sine wave.
The period of the wave is a good deal longer than a year.

This replaces my post of 11-08-2009 15:41 (links broken).

At Manilla, the traces of season temperature anomalies for daily maxima and daily minima differ, as these two graphs show. The convention is the same as for the earlier graph of daily mean temperature.



The peaks and troughs are different in height and date. Furthermore, the linear trend lines differ: that for maxima slopes down and that for minima slopes more steeply up. (This has been found before in climatic temperature change.)
In the above graphs the 1:2:1 smoothing does not extend to the two end points of the trace. The final data points, for winter 2009, do not include the last 12 days of the season. They, and the last smoothed point can be expected to rise a little with complete data.
The two traces are compared in the following graph:


The max and min traces tend to coincide when the temperature anomalies are small, as in climate years 1999,2000, 2001, 2003 and 2004.
In 2002, the max anomaly is much higher than the min, and the min lags by a whole season.
In 2006-7, the min anomaly is higher than the max and lags it by more than a whole season. The same happens at the 2007-8 trough, which is the deepest for both max and min.
I have found that, in the Manilla annual temperature cycle, daily minima lag daily maxima by almost a week. This lag of min temperature anomaly peaks and troughs by several months is in the same sense but very much longer.
I suspect the cause is related: daily maxima are caused fairly directly by received solar radiation. Daily minima are not. Perhaps they relate to a term I have just discovered: OLR (outgoing longwave radiation)?

This replaces the post of 18-08-2009 22:05 (broken links).

Since the time series for anomalies in daily maxima and daily minima differ in magnitude and in phase, the difference between them, the daily temperature range anomaly, can be shown like this:



The graph is a log of seasonal anomalies in daily temperature range at Manilla for the last 42 seasons. Manilla has the extremely wide daily temperature range of 15.5 degrees. It varies little though the year.
There was a peak positive anomaly in winter 2002. The mean anomaly for that season was 2.14 degrees. That is, the mean daily range that winter was over 17.6 degrees. In the year beginning March 2002 more than 50 days had daily temperature ranges wider than 20 degrees. The same was true for 2006. The graph shows that th 2006 peak anomaly was lower but broader.
There are three troughs lower than -1.5 degrees in the daily temperature range anomaly. They correspond to mean seasonal daily temperature rangesof less than 14 degrees. All are very recent: winter '07, summer '07-08, and spring '08.
The linear trend line shows a fairly convincing decline in daily temperature range. The variation explained is low, however: R-squared is 0.06! A sixth-order polynomial trend recognises that the major change is a dip that comes in the last two years.

Wide daily temperature ranges generally relate to lack of cloud. I have observed amount of morning cloud in Octas. The log of the anomaly follows:



Clearly, the two graphs are almost mirror reversed, even in the trend lines.

This replaces my post of 23-08-2009 20:32 (broken links).

Temperature anomalies, particularly those of daily maxima, clearly express the effect of received solar radiation. They show whether the climate is warm or cool.

Anomalies of daily temperature range and cloudiness relate rather to atmospheric moisture: whether the climate is humid or arid.

Rainfall anomaly is obviously another expression of moisture:



The pattern is not very different from that of cloud or the inverse of that of temperature range. The rainfall shortage of 2002 is plain to see.

All three factors are plotted together here:



The recognised drought of 2002 affects all three indicators. Less credence has been given to the peak of humid climate around spring 2005 and the extended humid climate through 2007, 2008 and early 2009.

This replaces my post of 23-08-2009 21:04 (broken links).

Temperature trends for all the days of the year at Manilla NSW.

These graphs are based on those used in a 2001 article by Knappenberger, Michaels and Davis ,“Nature of observed temperature changes across the United States during the 20th century”
I can't tell whether the technique was original to the authors.

Columns in the graphs show the slope of trend lines fitted to each of the days of the year, ordered by temperature. I have used 9-year data sets. Shorter sets give less stable results. Each graph compares two data sets: the grey columns are earlier trends to August 2008 and the black columns current trends to August 2009.
To simplify, I have used 364-day years, omitting February 29 and August 31. There are 91 columns, each the average of four days. Text boxes noting temperatures and seasons are approximate only.



Graph No. 1 shows trends in daily maximum temperature (“Days”). Winter days show a strong cooling trend for both periods, but the trend is now weaker. Through spring and autumn trends are weak, but trends in the current data are cooler than in the earlier data by an amount (about -0.06°/Yr) that is almost the same for most days. In summer, there is even more cooling (about -0.08°/Yr): a rather weak trend to cooler mid-summer days has become a strong trend to cooler days all summer.



Graph No. 2 shows trends in daily minimum temperature (“Nights”). A trend to warmer nights in mid-winter had appeared in the earlier data. This warming now extends to most nights of the year, except summer. There is now a strong trend to much cooler mid-summer nights.

Taking days and nights together, we still have a trend for winter nights to be not much colder than winter days, as would happen at a place nearer to the ocean. Spring and autumn nights are also getting warmer relative to days. In summer, however, both days and nights are trending cooler.
Average figures show how Manilla at this time relates to the Australian warming trend since 1950 (+0.0160°/yr). In the 9 years to August 2008, days cooled at -0.0201°/yr, nights warmed at +0.0410°/yr, and the average rose at +0.0105°/yr. In the 9 years to August 2009, days cooled at -0.0463°/yr, nights warmed at +0.0641°/yr, and the average rose at +0.0089°/yr.
Manilla’s “global warming” happens mainly at night, and not in summer.

To quote Knappenberger et al:
“These findings add to the growing evidence...... that the surface air temperature change that has occurred during the period of the greatest human influence on the climate is one in which increases of [extremely] low temperatures have dominated over those of high temperatures—a climate tending toward moderation rather than the extreme. Prognostications of dire consequences built upon model projections of a climate change dominated by increasing high temperatures should be reassessed based upon a growing body of evidence to the contrary.”

As footnote to
>...a climate tending toward moderation rather than extremes...<

Manilla recently had these record-breaking daily temperature anomaly events:
Daily max 15.2 degrees below normal, 14/2/09;
Daily max 11.4 degrees above normal, 23/8/09, and daily min 14.0 degrees above normal, 25/8/09.
That is, abnormally low temperature in summer, and abnormally high temperature in winter.

Extreme events like that are not hard to take at all.

Since no-one has felt inclined to discuss the graph above, I'll try to kick off the discussion myself.

This is a plot of just two key climate indicators, anomalies of (1) max daily temperature and (2) season total rainfall, for the last ten years at Manilla, NSW. I think the pattern will be found to be rather similar for places from central Queensland to Sydney. Unless we have been misled, the "Murray-Darling" drought-stricken area must be quite different.
I have drawn a smooth line through successive data points, making the line thicken with advancing time. The colour changes each year, from violet through to red. The data is smoothed (1:2:1)/4, except for the last data point (Spring '09) which is necessarily raw data. The standard period for rainfall is the 125-year record. The standard period for temperature (etc.) is 10 years from March 1999.

About twenty-five of the forty data points cluster about the origin, showing the general stability of the climate with time.
There are several distinct, well-organized excursions away from the origin.
1. The 2002 extreme drought. Winter and Spring 2002 were very hot and dry. The drought is evident on the graph from summer 2001-02 (already very low rainfall) to summer 2002-03 (still high temperature). The elliptical loop on the graph shows clearly that the low rainfall anomaly leads the high temperature anomaly by almost three months.
2. Extremely wet spring 2005. Three seasons were involved, with little change in temperature anomaly.
3. Extremely cold summer 2007-08, with normal rainfall.
4. Very high rainfall in spring 2008, with temperature steadily rising.
5. Winter and spring 2009 dry and extremely hot. The warming trend has persisted for nearly two years.

Over to you.

A graph above traces the sequence of smoothed monthly mean maximum temperatures and rainfall totals for Manilla, NSW in the last decade. I want to put this in a wider context.

Monthly rainfall and temperature data for specified areas of Australia can be found in the "Climate/Climate Change/Australian Climate Variability and Change" section of the website of the Australian Bureau of Meteorology. They are in the "Data Portal" on this page:
http://reg.bom.gov.au/silo/products/cli_chg/
Monthly values are given for temperature from 1950 and for rainfall from 1900. Averages are cited for the standard period 1961-1990. I have subtracted these averages from the monthly data to get anomaly values.
I selected the whole of Australia.

METHOD
The graph below tracks the relation of rainfall anomaly to maximum temperature anomaly Australia-wide for the same time period as the Manilla graph: mid-1999 to the present.
This time I applied a smoothing function directly to the monthly data. It is a normal distribution function with Standard Deviation 2.5 months. This gives a sampling window (low-pass filter) with a half-width of six months. (For the final six months of data, I applied progressively narrower sampling windows.) Because the rainfall anomalies are monthly rather than seasonal values, the vertical scale is one third of that in the previous graph.

RESULTS
Australia wide, the pattern of climate variation through the decade is more regular than at Manilla. There is a strong alignment from high rainfall/low temperature to low rainfall/high temperature.
The anti-clockwise looping structure is persistent: there are six such loops. As an example, I have labelled the points for February and April 2002 (yellow) to show how the loops are caused by maximum temperature lagging behind minimum rainfall. Minimum temperature also lags behind maximum rainfall at the top of the loops.

To clarify the pattern, I have made a second graph showing only data from 2006 to date.

The last five data points show anomalously high temperatures. These temperatures may fall somewhat when later data allow the use of the same six month sampling window on these data points as on earlier ones.

That is a very interesting idea Surly to do a graph like this, I was not aware of your post until now otherwise I would have commented sooner. It might be also interesting to plot a number of individual stations on the same chart where instead of anomalies you use scaled values. The interesting thing would be so see if some stations overlap each others climate in various years. For example I am thinking that the climate of Sunny Corner is now almost like Bathurst and Bathurst like Wellington etc. Perhaps Manilla is now more like what Moree use to be like?.

Hi snowmi
I hope people will do their own comparison of stations. I suggest that comparing anomalies may trump comparing scaled values, but it is worth a try.

I have in mind to use the (cunningly hidden) BoM "Data Portal" data to get data for the area of the great "Murray-Darling" drought: roughly from 33S;140E to 39S;150E - way south of here.
For the time being, there is plenty I want to discuss in the Australia-wide data.

For Manilla data, it will be a couple of months before there is much new to say.

Here are the traces of the relation of smoothed monthly rainfall anomaly to monthly mean maximum temperature anomaly Australia-wide, for each decade since 1950. Rainfall data goes back to 1900 on the linked web-page, but temperature begins at 1950.

The following graphs plot the time series of monthly data for the whole of Australia, smoothed with a gaussian window of Standard Deviation 2.5 months.
The two independent series of (a) mean maximum monthly temperature anomaly in degrees celsius and (b) total monthly rainfall anomaly in mm are plotted on the same graphs, but the scale for temperature is inverted for easy comparison.
One degree on the left axis corresponds to 10 mm on the right axis, but the zero lines may differ.


There is an obvious sine-wave cycle with a wavelength of between one and three years. (This is the "quasi-biennial cycle" that A.B. Pittock identified in 1971.)
Most peaks and troughs on these independent time series almost coincide, and their relative heights and depths tend to agree. In fact. the correlation between values of temperature and rainfall is poor, but the shapes of the sinusoidal curves match extremely well.
Peaks and troughs on the rainfall curve tend to lead those on the (inverted) temperature curve by one, two, or three months. In the graphs below, rainfall values have been lagged one month, to show that many of the peaks and troughs become better aligned.



Taking the whole of Australia in the last 60 years, it is fairly clear that:
points of lowest maximum temperature have generally lagged about one month behind points of highest rainfall;
points of highest maximum temperature have generally lagged about one month behind points of lowest rainfall.

Given this lag effect, times of lowest rainfall cannot be caused by times of highest temperature, but it is possible that times of highest temperature may be caused by times of lowest rainfall.
I find it plausible that temperature swings would closely follow rainfall swings (but in the opposite sense) due to lack of cooling by evapotranspiration in times of drought and effective cooling by evapotranspiration in times of deluge.

People are concerned (panicked) that climate trends seen in the sixty years since 1950 may develop into a major change.

Such major changes have recurred as a 100,000-year cycle with a 10 degree range in temperature. A cold episode is a glacial (Ice-Age), and a warm episode an interglacial.

The Australian climate record since 1950 shows "quasi-biennial" cycles of drying (to drought) with warming, followed by wetting (to deluge) with cooling.

Glacial/interglacial cycles are not like these drought/deluge cycles. Glacials are cold DRY times and interglacials warm WET times. In the Australian data plotted here, deluges are cold WET times and droughts are hot DRY times.

Glacial times, including the Younger Dryas only 12,000 years ago, had bigger lakes than now. This was not because there was more rain in ice-ages: there was less. The lakes were bigger because low temperature kept the evaporation down.

On a time-scale of a few years rather than thousands high lake levels may well reflect high rainfall rather than low evaporation. Perhaps on this short time-scale there is a positive feed-back leading to both droughts and flooding rains - as the Australian poet says.

I have updated my plots of smoothed rainfall anomaly versus smoothed max temp anomaly at Manilla NSW.

This time I have treated the data the same way as in my plots for Australia as a whole. Instead of seasonal values smoothed (1:2:1)/4, I have used monthly values smoothed with a gaussian window of half-width 6 months.
For clarity, I have used shorter time-sequences than before on each graph.

Here is the plot from September 1999 to December 2005:

)

For most of this time, smoothed monthly anomalies did not exceed +/-10 mm for rainfall or +/-0.5 degrees for maximum temperature.

An extreme drought began in January 2002 with the smoothed monthly rainfall anomaly falling below -10 mm. In July, the negative rainfall anomaly peaked at -27 mm. The max temp anomaly had been rapidly rising, and it peaked at +1.44 degrees in October. By February 2003, both rainfall and max temp anomalies were again small, and the drought was over.

March 2005 again showed a peak negative rainfall anomaly, but only -13 mm. While max temp anomaly remained steady, rainfall anomaly rose rapidly to peak at +20 mm in November 2005.

The next plot shows data from January 2006 to January 2010.



July 2009 is the last month to which the chosen smoothing function can be applied. Later months have been smoothed with windows that get steadily narrower, until the last data point is a raw unsmoothed value.

From very wet conditions in the summer of 2005-06, rainfall anomaly rapidly fell until October 2006 was as dry as in March 2005. The two years 2005 and 2006 were uniformly warm while rainfall fluctuated.

The next large anomaly is the very low max temp anomalies of January and February 2008: -1.37 degrees.

Rainfall anomaly peaked again at +17 mm in October 2008, then declined to -15 mm by July 2009, while the max temp anomaly steadily rose.

The last six monthly values cannot easily be compared with the earlier ones. They swing about ever more wildly. There is no doubt that these max temp anomaly values are very high, the highest in this 11-year record, but they may have peaked already. Rainfall anomaly values may also have peaked without being extreme, as they were in 2002.

Data for February 2010 can now be added to local climate change graphs.

Unsmoothed monthly anomaly values.


Many posters on these forums are recording monthly data values, so I have plotted Manilla monthly anomaly values in this graph. The extreme values here have been discussed in the forums, as they have happened over a wide area:

Extremely wet months: January 2006, November 2008;
Extremely dry months: January 2007, March 2008;
Exremely hot months: November 2009, August 2009;
Extremely cold months: June 2007, February 2008.

When it comes to finding climate trends the graph above is useless. There is too much noise: each month is quite unlike the next.

Smoothed monthly anomaly values.


This graph shows smoothed data. The trends to warming since January 2008 and to drying since October 2008 are beyond doubt. The 6-month smoothing window cannot yet be applied to data later than August 2009. Since I have used progressively narrower sampling windows, later data points are less reliable for showing the trends.

Both the warming trend and the drying trend seem likely to have reversed very recently.
I guess that the climate at Manilla began to moisten up about September last year and began to cool down about November last year. The "Quasi-biennial" looping patterns shown on data plots in this thread lead me to suggest that the climate here will now trend back towards being wetter and cooler than normal through this winter and perhaps next summer.

Much more cloud lately

For several years Manilla's climate has been very cloudy, despite the fact that it has not been very rainy.

I reported some recent very large increases in cloudiness at Manilla in this other thread .
I received some long, thoughtful comments from ROM, but few other replies.
Extreme cloudiness has continued at Manilla since the last post on that thread and, in one sense, has become even more extreme.

These graphs plot a sequence of data points with smoothed cloudiness anomaly values on the y-axis and smoothed rainfall anomaly values on the x-axis.

The first graph, with data from September 1999 to December 2005, shows how cloudiness tended to vary with rainfall during that time. In the units used, the relation was roughly y=0.5x. Nearly all values fell between the lines y=0.5x+8 and y=0.5x-8, as shown. The climate swung between cloudy-and-wet and sunny-and-dry.



The second graph shows data from January 2006 to February 2010.

(1). While the cloudiness anomalies on the first graph did not rise above +6, nearly all of the cloudiness anomalies since October 2007 have been higher than that.

(2). Cloudiness has become much greater in relation to rainfall. Thirty of the last forty months had anomaly values higher than the relation y=0.5x+8, which had not happened before. For these months, the relation is near y=0.5x+12. Since each extra cloudy day in a month accounts for a three percent change in number of cloudy days, these months have about four more cloudy days than in the standard period. I find this increase astonishing!

Very likely, my 10-year standard period is not representative. However, the very large changes seen do not depend on this.
Manilla seems to be in more immediate danger of losing sight of the sun than of being cooked by higher temperature.

Notes.
I observe the type and amount of cloud in octas each morning at about 9 am. For each month I record the percentage of morning observations that are more than four octas. I call this "Percentage of Cloudy Days". I have adopted a monthly normal value using averages from a standard 10-year period beginning June 1999. (The most cloudy month, July, has 35.2 percent cloudy days and the least cloudy month, September, has 23.7 percent.) The anomaly value is the difference between actual and normal percentage values. Smoothing is applied as descrbed in recent posts.

Cloud vs. max temperature at Manilla

These graphs are like those relating rainfall anomalies to max temperature anomalies in Posts #803874, #836843, and #840068. Cloud anomalies seem to jump in and out of step with temperature anomalies.

In the earlier graphs, high rainfall related to low temperature and vice versa, but temperature generally lagged behind rainfall. The trace typically formed a series of anti-clockwise loops, elongated from dry-then-hot to wet-then-cold. Sharp reversals were few and small.

In the graphs shown here, cloudy days and temperature relate in three distinct ways:


(1.) Cloudy days relate to low temperature and sunny days to high temperature in a rather strict linear fashion.

From May 2001 temperature
decreases with increasing cloudiness to a minimum in September 2001,
increases with decreasing cloudiness to a maximum in October 2002, then
decreases with increasing cloudiness to a minimum in May 2003.

From June 2005 temperature
decreases with increasing cloudiness to a minimum in September 2005, then
increases with decreasing cloudiness to a maximum in January 2006.

From December 2006 temperature
decreases with increasing cloudiness to a minimum in January 2008, then
increases with decreasing cloudiness to a maximum in May 2008.

These three episodes of near-linear relations lie close to a line y=-0.7x. They account for 49 months in the 129-month record.

(2.) Much of the remaining trace is in a looping mode, with little elongation. Two loops are clockwise and two anti-clockwise. In a clockwise loop (e.g. the year 2000) the cycle was cold-cloudy-hot-sunny. In an anti-clockwise loop (e.g. January 2005) the cycle was cold-sunny-hot-cloudy.

(3.) In the 21 months since May 2008 days have continued very cloudy while the temperature has ranged from very low to the maximum in the record. At the extreme temperatures of spring 2009 the observed anomalies in cloudy days are some 18 percent above those expected from the linear trend y=-0.7x.
While the graphs relating cloud to rainfall (Post #842256) suggested that these recent months had about four more cloudy days than expected, these graphs suggest there are about six more.

These are big changes in cloudiness, suggesting a climate moving from continental to maritime. They go with reduced daily temperature ranges, as shown in Post #3784. Post #3785 shows that Manilla's daily temperature ranges have reduced during the last nine years in all seasons except (so far) for summer.


Bleat!

Although I am happy that this thread gets up to 30 "Views" per day, I am surprised that there are almost no "Replies".
I really would appreciate questions or comments on this stuff.
Is there anything that should be explained?
Do you have data that differ from mine?
Is there more advanced work in this field?
Are the results already predicted by known patterns of soi, IOD, etc? Or not! If not, why not?

Yours
Surly

Dew Point vs. Max Temperature

Although Dew Point, Cloudy Days and Rainfall all indicate climatic moisture, each relates to maximum temperature differently during this eleven-year record for Manilla NSW.

Smoothed monthly Dew Point anomalies are plotted here against smoothed monthly maximum temperature anomalies.
As for Cloudy Days, there are extended periods with linear relations. These do not coincide with those noted for Cloudy Days and, unlike them, they vary widely in slope.

(1.) The trend line during the 2002 drought matches y= -0.9x-0.2. That is to say, the Dew Point falls nearly one degree for each degree of warming. The linear trend begins March 2002, more than a year later than the similar linear trend in cloudy days. It continues as temperature reaches a maximum in October 2002 and a minimum in June 2003 and then increases to October 2003: a total duration of twenty months.
(2.) An earlier linear trend line slopes the other way. From a low temperature in February 2000 the trend line is y= +0.5x+0.6. After a maximum temperature (with maximum Dew Point) in November 2000, the linear trend reverses until August 2001, for a total duration of eighteen months.
(3.) A very steep positive linear trend marks the seven-month cooling period from December 2005 to June 2006, at y= +3.1x-1.5.
(4.) A negative linear trend extends eleven months from November 2008 to (probably) September 2009: y= -0.7x.
Thus, linear relations occur for four extended periods, totalling nearly half of the record. These suggest functional relationships between humidity (as shown by Dew Point) and temperature, but relationships that take different coefficients, both positive and negative, at different times.

Dew Point versus temperature trends that have negative coefficients seem like the quasi-biennial hot-drought-to-cool-deluge cycles often mentioned in this thread. Trends with positive coefficients seem more like micro glacial (ice age) to interglacial cycles: cold-drought-to-warm-deluge.

Not that we actually know for sure that the last ice-age was droughty, or whether that drought had low precipitation, or low humidity, or sunny skies, or strong winds, or all of those. We can be quite sure of two things about the last ice-age (say 60,000 to 20,000 years ago): there was an awful lot of ice about on land (but not here), and the sea level was way, way down.
We are profoundly ignorant of the glacial and interglacial climates during the very brief life of the human species. (Pontification ends.)

Notes. I use Dew Point as an index of absolute humidity of the atmosphere (at sea level). For stability, I prefer early morning observations. I estimate early morning Dew Point by applying the daily maximum Relative Humidity reading to the daily minimum Dry Bulb temperature, using a cook-book formula. For values before July 2005 I adapt 6 am readings from Bureau of Meteorology records at Tamworth Airport. My standard period is the 10 years from March 1999. Manilla normal early morning Dew Points vary from 14.9 degrees in February to 2.4 degrees in July and August.

I have been posting on this site for most of a year usually in the climate comment sections, but since you asked for other opinions, I have been looking at the 18.6 year pattern of lunar declination (LD) and it's production of tidal effects in the atmosphere.

I posted a few comments on Morning Glory wave production several years ago, tying them to LD tides and was able to forecast them on drop bears site. Lately I have been looking at tornado generation patterns in the USA in relation to LD tides.
Latest hypothesis on how the global circulation is driven by complex patterns interacting through the interactions of the lunar tidal periods and solar wind strength due to Earth's interactions with the outer planets.

http://research.aerology.com/aerology-analog-weather-forecasting-method/

With global patterns of circulation there are periods that repeat that you might find helpful toward the end of the above link there is a link to some Pacific Ocean animated satellite photos that if viewed 18 or 19 years apart, IE the month 18 years ago should look a lot like this month, if you adjust for the 11 day slewing of the period in the pattern you could get a preview of up coming patterns of global circulation better than guessing months in advance.

I find it most refreshing that you are using dew points as it has a lot to do with the specific heat capacity of the air masses, and the trans-evaporation trends...The AGW crowd seem to be missing that as a valid point of interest, although they are overly concerned with temps, they seem to not care about heat content???

Just some thoughts of my own to brighten your day keep up the good work.
Richard Holle

Thanks, Richard.
Even the sixty year data sequence of monthly data from BoM is not long enough to resolve an 18-year cycle. I tried doing Fast Fourier Transforms. I think I got valid results, but the strongest periodicity was 29.8 months (2.48 years). I then tried fitting a cycle of that wavelength to the data trace. I utterly failed to match the cycle to the data. Every attempt to tweak the cycle gave an increase in the residual variance, not the hoped-for decrease.

What you have discovered Surly is that the climate is far more random and chaotic than we would want to believe. As much as we wish to find regularity like sunrise/sunset and the seasons it just does not seem to be there appart from just that.

years of monthly data will show almost nothing, try using daily data, then look for 109.3 day cycles in anomalies from norm because of the seasonal shifts. Is there somewhere I can down load the complete BOM daily records? I would like to generate a forecast for Australia, many here have said that would be interesting. Complete details on how to do that is included in the link above.

Richard Holle

This may be of some help to you.

http://www.bom.gov.au/cgi-bin/climate/hqsites/site_networks.cgi

Subsoil Temperature Cycles


At Manilla subsoil temperature does not relate to air temperature quite as one might expect. Soil temperature lags well behind air temperature from March to August, then moves in lock-step from September to February (Even the "bump" in October temperature is seen in both.).
In spring and summer soil temperature (at 750 mm) is very close to daily mean air temperature; in autumn and winter soil temperature is about four degrees above it.

There is a seasonal lag.
This makes the minimum temperatures occur later than the winter solstice and maximum temperatures occur later than the summer solstice. Manilla has the pattern noted in Wikipedia:
"In mid-latitude continental climates, [the lag] is approximately 20-25 days in winter and 25-35 days in summer."
To judge by Wikipedia, Manilla's winter lag is unusually short.

Manilla's winter lags are:
Daily max: 17 days;
Daily mean: 19 days;
Daily min: 22 days;
Subsoil (750); 33 days.

Manilla's summer lags are:
Daily max: 31 days;
Daily mean: 35 days;
Daily min: 42 days;
Subsoil (750); 46 days.

For air temperatures, these summer lags are close to 1.9 times winter lags.
For subsoil temperature, the summer lag is only 1.4 times the winter lag.


These are the 11-year records of monthly mean air temperature and subsoil temperature for Manilla.
Peaks on the two curves almost coincide. Generally the subsoil peaks are a little higher. February 2007 is much higher; February 2005 is lower. Subsoil troughs are generally four degrees warmer than mean temperature troughs. In July 2007 the subsoil was six degrees warmer; in August 2005, only two degrees warmer.

I have fitted linear trend lines to the traces. In this time-frame, the subsoil temperature is about two degrees higher than mean air temperature. I don't know of a ready explanation.
The trends also differ. The mean air temperature is rising at about one degree in 75 years, in line with global figures. The subsoil temperature is rising at only half that rate: about one degree in 150 years.

Could the air temperature be trying very hard to catch up with the subsoil temperature?


Notes
I measure subsoil temperature at 750 mm depth in a lithosol on weathered shale. The site slopes west at about 10%. It is five metres west of the house, and is partly shaded by white box trees and shrubs. The sensor/transmitter is an "Ewig" housed at ground level, with a wire to depth in a plastic conduit, and a radio link to the house. The receiver stores maximum and minimum values. When I read these daily, the values rarely differ as much as 0.3 degrees.
Clearly, at this site the daily temperature cycle is almost damped out at a depth of 750 mm.
Data begin 27/5/05. Data before that date are estimates based on Gunnedah Soil Research Stastion readings at 500 mm, with corrections for zero error, range, and lag.

Subsoil Temp Anomalies vs. Max Air Temp Anomalies

Sometimes smoothed subsoil temp anomalies follow smoothed max air temp anomalies with a month or two of lag. Sometimes there is no lag. Sometimes the subsoil does not respond.


A cooling loop from September 1999 reached min air temp in December 1999 and minimum subsoil temp a month later. Subsoil and air warmed together until October 2000, when subsoil temp hardly changed in the 16 months to February 2002. The warming loop then resumed, reaching max air temp in October 2002 (drought) and max subsoil temp a month later. The loop continued with cooling to min air temp in May 2003 and min subsoil temp three months later. A series of small loops ended with slightly low subsoil temps and slightly high air temps by December 2005.


For four months from January 2006 subsoil temp anomaly did not change while air temp anomaly fell. A very big loop then began, with max air temp anomaly in December 2006 and an extremely high subsoil temp anomaly (+1.80) three months later in March 2007. In the thirteen months to April 2008 the subsoil temp anomaly fell dramatically by 2.9 degrees to -1.09. This followed two months after the very low minimum in the air temp anomaly of February 2008.
A linear increase in both air and subsoil anomalies prevailed from May 2008 to June 2009. Since then, a swing to very high air temperature and down again has not been reflected in subsoil temperatures.

Very large swings in temperature anomalies must surely stress plants that are adapted to particular localities. Broadly, a temperature difference of three degrees is like a latitude difference (in Australia) of five degrees (600 km) or an altitude difference of 500 metres. Some trees thrive only in a zone as narrow as that.
This graph shows that Manilla has just swung from a negative anomaly in daily maximum temperature of -1.4 in February 2008 to a (provisional) +2.3 in November 2009, a rise of 3.7 degrees. Perhaps the plants are equally affected by subsoil temperature. In this case the corresponding swing in subsoil temperature was only about 1.9 degrees. However, the smaller down-swing in daily maximum temperature anomaly of 2.2 degrees during the year 2007 was accompanied by the very serious down-swing of 2.9 degrees in subsoil temperature anomaly.
Certainly, I was unaware that such a large down-swing in subsoil temperature anomaly had occurred during 2007, I wonder if anyone else noticed it, or found unusual evidence of stress in plants at that time.

March 2010 Update
Smoothed monthly climate anomalies for Manilla NSW

These four graphs update similar plots in recent posts.

Each month is marked by a diamond, but each January is marked by a larger square. Solid red symbols mark values that are smoothed using a gaussian window of half-width 6 months. Uncoloured symbols mark values for the latest six months, which are smoothed with progressively narrower windows, until the final month's value is not smoothed at all.

1. The anomaly of maximum daily temperature appears on the x-axis of each graph. On earlier graphs the values for November and December 2009 had exceeded +2.0 degrees, but increased smoothing has now brought them lower. Anomalies for the months of 2010 are closer to zero. THE HEAT WAVE IS OVER!

2. Rainfall anomalies paused at rather high negative values during the summer (i.e. SUMMER WAS RATHER DRY). The raw data point for March is more negative (little rain), but it is not clear whether the trend is up or down.


3. Cloudiness anomalies remained strongly positive through the last thirty months. And it's STILL VERY CLOUDY!


4. Humidity (Dew Point) anomalies rose quite rapidly after a low value in October 2009. It's HUMID AGAIN!


5. The subsoil temperature anomaly failed to rise with maximum air temperature anomaly from June to November 2009. The two anomalies now seem to be falling together back down the line of the rising trends of early 2009.

Notes
These graphs show only data for the last 36 months; long enough to include one climatic cycle. As in much of Australia, Manilla's climate variation is dominated by "Quasi biennial oscillations" named by Barrie Pittock in 1971. Wasyl Drosdowsky uses the Three-Letter-Acronym "QBO". In the full version of
this paper he uses spectral analysis to show that cycles in the two to three year range are important in Australian regional climates, as they are in the Southern Oscillation record.

The Quasi-biennial oscillation (QBO)

Why hasn't anyone chipped me on my ignorance about this subject?

Here it is in Wikipedia:
http://en.wikipedia.org/wiki/Quasi-biennial_oscillation

"The QBO (quasi-biennial oscillation) is a quasi-periodic oscillation of the equatorial zonal wind between easterlies and westerlies in the tropical stratosphere with a mean period of 28 to 29 months."

It seems that Pittock and Drosdowsky are simply referring to Australian observations of a known phenomenon.

Me too.

Smoothed monthly anomalies of daily temperature range

In these plots, I have labelled climates with the widest daily temperature ranges as "Extreme". Extreme climates with the lowest daily max temperature anomalies are "Cold Extreme" climates, as in Siberia; extreme climates with the highest daily max temperature anomalies are "Hot Extreme" climates, as in the Sahara.
Climates with the narrowest daily temperature ranges are labelled "Equable". Equable climates with the lowest daily max temperature anomalies are "Cold Equable" climates, as in Macquarie Island; equable climates with the highest daily max temperature anomalies are "Hot Equable" climates, as in the central Pacific Ocean.

In the first plot, daily temperature range anomaly tends to relate linearly to daily maximum temperature anomaly, with slope 1:1. This pattern prevails from April 2000 to December 2005. Clockwise loops show that temperature range anomaly leads max temperature anomaly by one to six months.
In the drought of August-September 2002, the climate approached "Saharan", and then rapidly swung towards the "Macquarie Island" type eight months later.
The nearest approach to the "Siberian" type was in November 2001 (also December 1999) when the temperature range anomaly was 0.8 degrees higher than the maximum temperature anomaly.

In the second plot, the linear relation is only intermittent. Positive temperature range anomalies are now much smaller (+0.8 vs. +1.2) and negative anomalies much larger (-1.1 vs. -0.6). The large negative temperature range anomalies came with low max temperatures, making the climate approach the cold equable "Macquarie Island" type. This persisted from June 2007 to January 2008, and from September to November 2008.
As in the first plot, temperature range anomalies 0.8 degrees above max temp anomalies ("Siberian" type) occur: in May 2006 and March 2008.
In a new development for this data set, a swing to high temperature has not brought a high temperature range: the climate is as close to the hot equable "Pacific" type as to the hot extreme "Sahara" type. In June 2009 the temperature range anomaly fell to 0.8 degrees below the max temperature anomaly. This relation seems to have prevailed to the present time.


Note.
I have realised that these graphs are parametric plots. Each of the two variables plotted is a function of the parameter: Time. The curves track the parameter, and I have labeled noteworthy values of the parameter.

Smoothed minimum vs. maximum temp anomalies

These parametric plots compare anomalies of daily minimum temperatures with those of daily maximum temperatures. This data was included in previous non-parametric plots in Post #3782 (9/9/09), but these parametric plots clarify the leads and lags between maxima and minima.

In the latest Post, #859739, "extreme" climates, with wide temperature ranges were at the top of the parametric plots, and "equable" climates with narrow temperature ranges were at the bottom but, in the plots of this Post, "extreme" climates and "equable" climates are at the bottom left and top right, separated by a 1:1 trend line on which daily maxima and daily minima move together and the daily temperature range anomaly remains normal.

The first plot (1999-2005) shows temperature anomalies moving close to the 1:1 trend line for much of the time. The exception is the cycle in and out of the 2002 drought. This forms an anti-clockwise loop that is almost circular. After temperature anomalies both reached a low point in October 2001, minima did not rise as rapidly as maxima until, from April to June 2002, minima were not rising at all. During the drought peak, from July to September 2002, maxima and minima rose together, but with the anomaly of minima now 1.2 degrees lower than that of maxima. (That is, the daily temperature range was 1.2 degrees wider than normal: an "extreme" climate.)
From October 2002, the daily minimum anomaly fell much more slowly than the daily maximum anomaly, moving to an "equable" climate by April 2003, and then the daily maximum anomaly stayed the same for four months while the daily minimum anomaly fell, returning the plot to the 1:1 trend line by August 2003.

In the second plot, the year 2006 had max and min temperature anomalies falling, then rising again along a steeper trend line. This had the effect of moving the climate towards "extreme" in May 2006 (the month with lowest anomalies) and back again. In the first half of 2007, maximum anomalies fell steadily from a peak in January, while minimum anomalies peaked in April. After June 2007, anomaly values fell in a period of "equable" climate as noted in the previous post. The lowest anomalies of the decade came in February 2008 for daily max (-1.41) and in March 2008 for daily min (-0.92). Since then, apart from an excursion towards "equable" climate in October 2008, daily max and daily min anomalies have risen together to decade record high values, perhaps peaking in November 2009.

Time-sequence of climate indicators

As the climate at Manilla goes through a quasi-biennial oscillation of "droughts and flooding rains", the anomaly values of various climate indicators reach maximal or minimal peaks that are more-or-less in step.
A "drought" peak has maximal values of Daily Max Temperature, Daily Min Temperature, Daily Temperature Range, and Subsoil Temperature, and minimal values of Rainfall, Cloud, and early morning Dew Point.
A "flooding-rains" peak has these minimal and maximal values reversed.

However, the indicators reach peaks at somewhat different times. In this short record a typical sequence seems to be as follows:

For a "drought" peak:
First: Daily Temperature Range (max);
One month later: Rainfall (min), Cloud (min);
Two months later: Daily Max Temperature (max), Dew Point (min);
Four months later: Daily Min Temperature (max), Subsoil Temperature (max).

For a "flooding-rains" peak:
First: Daily Temperature Range (min);
One month later: Rainfall (max), Cloud (max);
Two months later: Daily Max Temperature (min), Dew Point (max);
Four months later: Daily Min Temperature (min), Subsoil Temperature (min).

As mentioned in Post #810237, it makes sense that a peak of Daily Max Temperature should follow after a trough in Rainfall, and a trough of Daily Max Temperature should follow after a peak in Rainfall as a result of changed evapotranspiration.

To me, the real surprise in this pattern is that the very first indicator to reach a peak (maximal or minimal) is the Daily Temperature Range.

What could cause that?

I am also surprised that the Dew Point peak (maximal or minimal) comes so late. I would have expected increased humidity to be a pre-condition for increased rainfall or cloudiness. Perhaps early morning Dew Point, measured near the ground, is a consequence of the developing extreme state of evapotranspiration rather than a measure of the prior humidity of the incoming air mass.

Manilla Smoothed Monthly Anomalies of Climate Variables
Parametric Plots
Update for April 2010

Notes.
These Parametric Plots show anomalies of climate variables observed at Manilla NSW in the last 36 months. Anomaly values are smoothed using a gaussian window of half-width 6 months. The last six values are smoothed with progressively narrower windows, until the last month is a raw value.
Earlier plots had y-axes with positive anomaly values always at the top. Now the plots all have values related to "drought" at the top, on the right side. Negative anomaly values are now at the top for Rainfall, Cloud, and Dew Point.
Fully smoothed data points are marked with red diamonds. Open diamonds mark points smoothed with a narrower window. An orange diamond marks the latest (raw) data value. Blue diamonds are extreme values of the smoothed data set, beginning in September 1999.

Daily Maximum Temperature
On each plot, the x-axis has the smoothed anomaly of mean daily maximum temperature. The trend was still rising through very high values at the last fully-smoothed data point in October 2009. Very likely a peak will come in November 2009. Partially smoothed data points suggest cooling and re-warming since then.

Monthly Total Rainfall
Fully-smoothed monthly rainfall anomalies have stayed close to -15 mm/month since June 2009. This is a mild drought, much less severe than in winter 2002 (-27 mm/month).


Cloudy Mornings %
All points on this plot are positive anomalies. The anomaly has remained near +8% for many months, and seems to be getting even more positive.


Early Morning Dew Point
Dew Point anomaly fell with rising max temp anomaly to September 2009, then began to rise. The raw value is now far into the realm of "Hot Humid" (or "Interglacial") climate. The record began with even higher humidity in 1999, but has seldom been high since.


Daily Temperature Range
From June to October 2009, temperature range rose with max temp, but barely broke into positive values. The climate was between "Saharan" and "Pacific". It now seems closer to "Pacific".
The instrumental readings on this plot, and those on the Dew Point plot support the observations on the Cloudy Mornings plot. They all show a recent shift from "Continental" towards "Maritime" climate.


Daily Minimum Temperature
Fully-smoothed anomalies of min temp fell and rose with those of max temp. Since October 2009 they seem to have continued to rise and fall together.


Subsoil Temperature
Since April 2009, subsoil temperature anomalies have been small.

Thread stats

I have been tracking the stats for this thread. I am happy to say that the mean views per day has trended up from 20 at the beginning of March to 30 now. There have been several days with very many views, including 155 on Sunday 11th April and 111 on Thursday 22nd April. I can see no pattern in this at all.

It is a bit lonely here.
There have been nearly 1500 "Views" since the last "Reply" (not counting my posts).
I really would appreciate feedback, Or any kind of discussion, whether amateur, like me, or professional.

Could you describe your 30-year oscillation in simpleton terms?

Hi Nazdeck, happy to oblige.

It is not 30 years, it is 30 months. It first came up in this thread in a graph that I had in Post #3745, but I stupidly wiped the graph. It is now re-presented in Post #3781 on Page 4. By that time Keith and Arnost had contributed graphs showing the same cycle. I guessed the period was about 5/3 years (i.e. 20 months).

In Post #810237 on Page 5, you will find graphs of the 60-year record of Australian smoothed monthly anomalies of rainfall and maximum temperature. It is quite clear that there is a two to three year cycle. Points of lowest maximum temperature faithfully follow points of highest rainfall about one month later. I did a fourier analysis that showed the cycle to be 29.8 months (Post #846194).

No-one volunteered an explanation of why this cycle might occur.

By the time of Post #810237 I had discovered that Barrie Pittock had mentioned a "quasi-biennial cycle" in Australian climates in 1971. However, I still do not have access to his paper.

On Page 7, Post #856424, in my "Notes" at the bottom I link to a (VERY technical) paper by Wasyl Drosdowsky that gives some detail about how quasi-biennial oscillations (about 30 months long) act differently in various regions of Australia.

Finally I looked in Wikipedia, and there it was! (See Post #858512, Page 7.)
"The QBO (quasi-biennial oscillation) is a quasi-periodic oscillation of the equatorial zonal wind between easterlies and westerlies in the tropical stratosphere with a mean period of 28 to 29 months."

The 30-month oscillation in my data (and the BoM rainfall and temperature data for the whole country) seems very likely to relate to the QBO defined as a reversal pattern in tropical stratospheric winds, but I have no idea how.

In broad terms, my climate data shows cycles of about 30 months, with rough coincidence of peaks in:
minimum rainfall,
minimum cloudiness,
minimum early morning dew point,
maximum max daily temperature,
maximum min daily temperature,
maximum daily temperature range,
maximum subsoil temperature.

I call this the cycle of "droughts and flooding rains". It is completely different to the cycle of glacials and interglacials. I have dramatised this in the labels in the four corners of the first graph in Post #861249 on Page 7.

Just ask about any point that is not clear. Cheers!

Manilla Smoothed Monthly Anomalies of Climate Variables
Parametric Plots
Update for May 2010

Manilla's climate in the last three years has been marked mainly by cloudier skies and a narrower daily temperature range.



Daily Maximum Temperature
On each plot, the x-axis has the smoothed anomaly of mean daily maximum temperature. The min value in Feb 2008 (-1.61) and max value in Nov 2009 (+1.35) are also the extremes of the data set. Partially smoothed data points suggest cooling to a near-normal temp since then.


Monthly Total Rainfall
Smoothed monthly rainfall anomalies, declining from a peak of +16.7 mm/month in Oct 2008, have stayed close to -15 mm/month since June 2009. Extremes of rainfall occurred earlier in the decade: a max of +20 mm/month in Nov 2005 and a min of -27 mm/month in Jul 2002.



Cloudy Mornings %
All points on this plot are positive anomalies (very cloudy). The anomaly has remained near +8% for many months, and seems to be getting even more positive. The very high anomaly for May 2010 is a raw value. Raw values for several other points on the graph have been even higher. Recent positive anomalies are in stark contrast to the extreme negative anomaly (-11.3%) of the drought month of Oct 2002.


Early Morning Dew Point
Dew Point anomaly fell with rising max temp anomaly to September 2009, then began to rise into the realm of "Hot Humid" climate. The record began with very high humidity (+1.67) in Sep 1999, but has seldom been high since.



Daily Temperature Range
From June to October 2009, temperature range anomaly rose with max temp, but barely broke into positive values. Then it fell rapidly, so the plot moved closer to the "Pacific" type. The positive extreme of temperature range (+1.23) was in Aug 2002 (drought).



Daily Minimum Temperature
Smoothed anomalies of min temp fell and rose with those of max temp. A new extreme positive anomaly of about +1.5 seems likely in Dec 2009.



Subsoil Temperature
The extreme positive anomaly of subsoil temp (+1.80) came in Mar 2007, just before the plotted sequence, and the extreme negative anomaly (-1.09) in May 2008, following two months behind the extreme negative anomaly of daily max temp.
Since April 2009, subsoil temperature anomalies have stayed close to +0.3.

Notes.
These Parametric Plots show anomalies of climate variables observed at Manilla NSW in the last 36 months. Anomaly values (red diamonds) are smoothed using a gaussian window of half-width 6 months. The last six values (open diamonds) are smoothed with progressively narrower windows, until the last month (orange diamond) is a raw value.
Blue diamonds are extreme values of the smoothed data set, beginning in September 1999. A blue rectangle is drawn through these points.
Some errors in calculating "normals" have been corrected, giving rise to discrepancies with earlier graphs.

SB, in your assessment of cloudiness, just what type and characteristics of the various cloud categories do you include in your cloud cover assessment.
And has there been a shift during your data period in the cloud cover type?

There has been a shift in cloud cover and type down here in west Vic this last summer with much higher cu's of a thermal origin showing up for a number of days in succession.
Bases up around the 8 to 12 thou feet.
This was the pattern of the late 60's to late 70's when I was really doing some long distance soaring.

[ Gossip! Cracked a 730 km triangle in 1974 in my Std Cirrus which unknown to any of us down here until a couple of years later was then apparently the biggest triangle ever flown in the world.
I was aiming for 800kms [ 500 miles as we were just moving out of the imperial measurements to metric ] but had to turn short to get home so no record and didn't even bother to try and claim anything as I already had my three diamonds by 1968 [ officially No 6 but reality No 4 in Australia and International No 994 ] in a Boomerang out of Horsham.
Started flying Tiger Moths in 1959 and gliders in 1963. Towing through a southern mid winter in a Tiger puts you off Tigers for life!
Best tow height, 14,000 feet in a Tiger with the Boomer on the back in weak wave down at the Grampians ]

This pattern seems to be tied in with a shift in a fairly stationary type of trough line system which positions here in southern Australia over the late summer period.
Troughs barrel in from the Bight and crawl to a halt around mid Vic and then sort of just sit there for days at a time..
East of the trough is excellent high, strong thermal conditions such as Benalla, Tocumal, Corowa have been experiencing for the last couple of decades.
West of the trough is stable, low inversion, relatively weak thermal conditions and bloody hard work soaring any real distances.
This semi permanent late summer trough type system now seems to be migrating back westwards and our soaring conditions this last season have improved quite dramatically.
So what are you seeing or are you seeing anything in the way of significant shifts in all the criteria you are recording?

Hi ROM

I have not yet tackled sorting out changes in the type of cloud. I will, but I haven't put those observations on the computer yet.
I note up to three cloud types as well as total octas. So I can use the traditional classes of high, middle and low cloud.
I will have to make up a scheme to assign proportions to my daily list of observed types. Of course, a low level overcast obscures any higher cloud. I will have to assume that that factor has not changed with time.
I will not be able to say anything about cloud-base height. I know it on days that I fly, but I don't keep a record of that. Cumulus cloud-base in November 2009 (14,000 feet) was some 50% higher than in recent summers, when it was about 9,000 feet.

Only one of my Diamonds was in the heroic days of wood (Canberra Gliding Club Boomerang).

Surly, I have some climate data I could put forward if you wanted to have a look at it.

Are you familiar with MATLAB?

Thanks, Nazdeck, but I encourage people who have data to do some quite simple examination of it themselves. Their findings would make this thread more interesting. Sites other than mine must have a similarly intricate dance going on between temperature, rainfall, humidity, cloudiness and other variables.

I am afraid I am not strong in maths. MATLAB is not for me.

Fair enough, that's ok .

I noticed you mentioned subsoil temperature as one of your variables. Are you aware of the theory, by D. L. Nofziger, on soil temperature variations with time and depth. There is a paper written on it with some very promising results/equations that might be of interest to you: see here.

Naz, how would one use Nofziger's model to predict future values of the data?

Nazdeck, thanks for linking to that paper by Dr Hofziger of Oklahoma State University. It is very relavant to my subsoil temperature observations in Post #855531.
I think his "Theory" is simply an implementation of the very old model of daily and annual waves of solar heat moving down through the soil. He presents data for a soil site in Hebei, China.
My surprise at the Manilla results was that they did not closely follow the model, given that I was using air temperature to represent the heat source. Mean annual temperature at 750 mm was 2 degrees warmer than mean annual air temperature.
Now, Hofziger's Fig. 3 and discussion also show that temperatures at depth are 2 degrees higher than would be predicted from air temperatures, rather than soil surface temperatures.
There is no pattern of anomalous lags in the Hebei data. The Manilla data show large lags in the cooling part of the curve, making soil temperature (at 750 mm) 4 degrees warmer than mean air temperature (in fact, closer to mean daily maximum temperature) at that season.

At Manilla, the very high subsoil temperature anomaly of 1.8 degrees in March 2007 remains a mystery.

I actually hadn’t even considered that prospect. I assume one would have to find the derivative of the equations in question with respect to the variable presented. The average soil temperature would first have to be measured (this is actually done locally in the Adelaide Hills out at Charleston, only I haven’t investigated much further).

The damping depth (as defined in document) could be calculated from the thermal diffusivity (given in square metres per second and calculated from the air density (in kilograms per cubic metres, which is dependent on the measured temperature and MSLP), the thermal conductivity (in Watts per metres per degree Kelvin, which can be calculated from the change in surface Enthalpy of moist air with respect to measured change from maximum to minimum temperature)). The other component of the damping depth (the frequency of temperature measurements) simply depends on the time interval between temperature readings (again, as indicated in the document).

Once the damping depth is calculated, we then need the depth at which soil measurements are taken. We also need to amplitude for soil temperature fluctuations, which is defined in the document for years. One would simply need to change the calculation to suit the required temporal scale (for any timescale this would mean taking half the difference between the average maximum and average minimum temperature over the period of interest). Then it is simply a matter of applying the differential equation given under “Assumptions and Simplifications.”

Much of this stuff is simply off the top of my head and I haven’t actually tested the model put forward Nofziger, however the graphs shown in the document present favourably for interpretations of changes in soil temperature with time.

I actually think that Nofziger’s ideas might be relevant to how much moisture is able to infiltrate a soil surface. There is an idea I have been testing with daily rainfall data which, at the moment, is pretty simple, but does seem at a glance to capture some of how much rainfall on a given day is able soak into the soil, and the rate of evaporation, only it probably needs some sort of damping depth (as described in the document) as a datum so the idea is not free-floating actually has a physical connection. The shortfall in this approach, however, is twofold: lack of knowledge of soil structure and lack of knowledge about soil moisture content, other than that derived from soil temperature.

What’s really interesting about Nofziger’s main equation is it’s potential application for forecasting daily temperature changes because of its temporal dependency (dependence of temperature on time).

Thanks Naz, I'm glad it's off the top of your head because although I think I can grasp the principles behind it all it's mostly way over the top of my head.

From what you say, it looks like an (artificial) neural network forecast would take account of the variables, assuming they have a significant input to the observations.

I am presently experimenting with an 'ANN' software package to see if I can estimate future rainfalls given various possible effects from ENSO as well as cycles in the data. The trouble is, it's very very hard to eliminate the insignificant regressors (money market data is so much easier).

I actually prefer using regression statistics/functions above a threshold if 80% accuracy. I have never actually dealt with ANNs apart from in reading journal articles.

I’m also rather interested in the type of climate data (daily, weekly, monthly; and the variables) and the length of the records that Surly has, as I wonder whether comparisons can be made across the continent with the records I have, covering the period 1/1979 to 11/2004.

I have tried to predict daily temperature variations (for maximum and minimum temperature, dewpoint, surface vapour pressure and MSLP using the 1st law of thermodynamics), only it is not straightforward as I first thought, because the equation for temperature variations needs to be time-dependent for the iterations to work properly.

Log of Smoothed Maximum Temperature and Rainfall
Last 3 years at Manilla

This simple graph shows the most basic climate trends since July 2007. Now that data for May 2010 are available, full smoothing extends to the end of spring 2009.



Maximum Temperature
The days with extreme high temperature in late November 2009 clearly relate to a big Quasi-biennial Oscillation (QBO) shown here. Smoothed temperature anomalies rose, almost without interruption, from a very low value of -1.61 degrees in February 2008 to a very high value of +1.35 degrees in November 2009. That was a rise of 2.96 degrees in 21 months: 0.14 degrees per month.
Before February 2008, the temperature had been falling rapidly for a year; after November 2009, data points (not yet fully smoothed) suggest that the temperature is again falling rapidly. It may be below normal already.

I find it very strange that quasi-biennial oscillations as large as this have not been mentioned in public. For comparison, I have included the 60-year trend of rising temperature in Australia that we are (rightly) advised to be concerned about. That trend rises at 0.0014 degrees per month, only one hundredth the rate seen in this current QBO!

Extreme Values
Lines near the top and bottom of the graph show the extreme values of both smoothed Maximum Temperature and smoothed Monthly Rainfall found in my 11 years of data. It happens that the extremes of Maximum Temperature occur within the last three years. Those of Monthly Rainfall do not.
As a rule, monthly rainfall anomalies exceeding +/-20 mm and Maximum Temperature anomalies exceeding +/- 1.0 degree are equally rare.

Monthly Rainfall
Smoothed monthly rainfall anomalies were very small for the first year, then peaked at +16 mm in October 2008. They fell to -16 mm by July 2009, and stayed there for at least four months and perhaps ten months. This is a persistent mild rainfall drought.
The last three years have shown little of the common pattern, which is for temperature anomalies to follow close behind rainfall anomalies, but with the opposite sign. That pattern prevailed only for nine months from November 2008 to July 2009, at which time the rainfall anomaly stopped decreasing, but the temperature anomaly kept rising to an extreme.
Less smoothed data points since November 2009 suggest a rapid fall in temperature anomaly with no matching rise in rainfall anomaly.

I have not followed the detail of changes that should have been expected in recent months due to El Nino, etc. I would be obliged to anyone willing to put my observations in that context. From what I have seen in other threads, surely someone could confidently say: "Yes, your graph shows just what we expected to see, because..." "And what we expect to happen next is...".

Surly, it has been discussed and pretty much proven in other threads that the rainfall increase you note in your graph that corresponds with a temperature fall is fairly typical. Certainly the further nth you go into the tropicals of Australia. The simple explanation is that the temperature falls with an increase in humidity and this is tied to moist air. Late Novemeber into December is typically dryer here and as such it is hotter.

The hottest temperatures in the past one hundred years have all been tied to elnino events. Due entirely to the drier air that typically is accompanied by westerly winds so therefore comes out of our arid regions. The single biggest friend to lower temperatures in summer is cloud cover up here and if the monsoon arrives on time it is significantly cooler (in temperature at least) by mid January albeit much more humid and sticky.

The central regions of NSW are sometimes subjected to a monsoonal influence and sometimes not. So modelling would be inconsistent.Your worst droughts are evident when the cold fronts move south of the continent and the monsoon rains stay across the top end or move out into the pacific. Because NSW is subjected to both of these variations it typically suffers the worst.

Your graph currently plots a sharp drop in temperatures in coming months and a corresponding rise in rainfall. It's fair to say that is the most likely outcome as the lanina is forecast to develop in that time frame. Currently models are showing a reasonably well defined lanina by summer.

Whilst many in here have looked for strong links between the IOD ,pacific oscillation soi and others ,the fact remains that the climate is far more complex. There is certainly a link between ENSO and rainfall and we know what ENSO is and even when it is forming one way or the other. What we don't understand fully is its timing. Why do 40 percent of elnino typically end with the formation of a lanina? Why do the other 60 % not?

What was going on in the period from 1950 to 1979 to make it the wettest 30 year period in the past 330 years? Are we really going back into another wet 30 year period? The past 3-4 years indicate we may be. Why was the timing of the formation of the past 3 ENSO events out of whack? Was it the heralding of a change to a 30 year wet period? Or a trigger if you like. There is still alot clearly that our very brief (in historic terms) study of the climate has left unanswered.

Much of the theory on GW originate from studies done in the 90's as we headed towards a super elnino. Since that heat was released the data has become flawed somewhat.

There have been plenty of graphs entered into the ENSO thread that have had a certainty attached only to be dismissed soon after. The climate is massively variable and that vast body of water to our east holds a lot of the secrets. Its relationship with the sun is undeniable.

If we ever do work out exactly how it works it will become boring.

congratulations on a great reply Cold front.I really enjoyed it.

lol, was trying to discuss this in a thread on ski.com but didn't have much of a response. Have you tried using a Kalman filter? It may be more effective for what your trying to do as you are only trying to estimate a single outcome (rainfall).

An ANN would be a much more effective package over a Kalman filter when you have many forecast variables as it not only has the ability to "learn" and recognise patterns, it can assigned weighted outcomes to very complex "paths" or forecast outcomes. ANN's are still in their infancy but greatly reduce the computing power needed to run something such as a climate model. When we programmed a Kalman filter and an ANN to perform a simple forecast for the movements of a robot based on inputs to its differential, the Kalmsn filter took around 12hs to complete 300 iterations while the ANN completed it in about 1h.

From what I remember at uni, in order to program an ANN it must first "see" the pattern initially, thus building the path, much like the human brain. Once the path is created it is then weighted accordingly depending on the number of times that particular patter is recognised. This makes it difficult to use as a climate model as it can take many years to program unless you have detailed and accurate data sets to feed it. This is especially the case of a climate model with an almost infinite number of outcomes. I would assume current data records (I.e. ssts, temp trend etc) would simply be insufficient to program such a network if the desired outcome was a forecasting tool for say the Eastern Seaboard of Aus.

It has been a long time since i have done anything with this type of stuff so forgive me if ive gotten something wrong there. Would be very interested to here more about your rainfall model though Keith. Ive always thought ANN's could be very effective forecasting tools. What program are you using by the way? Matlab?

Hi David,

What I am trying to do (and it looks like I'm getting there), is to predict future monthly rainfalls using ENSO variables as independent regressors. In other words I'm not trying to predict multiple climate variables. So I'm sorry if I confused anyone in the original post.

For the purpose of this, I run a correlation analysis to see which ENSO variables are significantly correlated with the rainfall, then use those significant ones as inputs in the ANN. I used principal components analysis to narrow down over 220 sea surface temperature variables into groups. I also used cycles in the rainfalls (Fourier trig functions).

On the question of filters, I've used a Hodrick Prescott filter in other work but it's a bit subjective and probably better suited to economic data. I've heard of Kalman but haven't gone into it.

I did use multiple regression with the variables at first but ran into problems in the cycles (where too many of them were linear combinations of others). So I resorted to the correlation analysis and weeded out the insignificant variables as described above. I notice that correlation analysis has been used in certain peer-reviewed papers on ANNs to which I had referred for preliminary help. The ANN software detects the contribution of each input variable, thus allowing removal of anything of a low order, but in the case of rainfall I thought this was counterproductive especially when regression and correlation was delivering totally different and at times, contrary results. It thus seemed to me to be better to eliminate the insignificant regressors before running an ANN. In the end it seems that one has to make value judgements of the data as the software doesn't necessarily know all the finer attributes of them.

I also use ARIMA for modelling but it's not much good for forecasting beyond a bare minimum period so it's mainly just for comparison or 'academic' interest.

I use a program called Statgraphics Centurion for the initial heavy duty stats stuff (PCA etc), then another program called Forecaster XL which is an add-in for Microsoft Excel spreadsheets. It will do a forecast of the entire rainfall dataset according to how it thinks it has 'learned' the patterns in the data, and also can be used to forecast months or even further ahead, based again on those same patterns. Certain parameters are customisable but for now I'm just letting the add-in decide the best ones. Of course, one would not want to project the forecast too far into the future.

Presently I'm experimenting with rainfalls in the tropical north coast of Queensland. The forecasted modelling thus far is picking up the seasonal and other patterns in the data and looks promising.

As soon as I can get certain automation of data (copying and manipulating using Excel VBA macros) going properly I hope to be able to post some results and write up a bit of an article. I've lost count of how many times changing a row or column number in the VBA code has stuffed up the whole thing, forcing me to debug it all again.

Sorry for a long reply but it's become quite a complex process.

Thanks for your comments, ColdFront.
It is good to get some input after a long gap. I appreciate your general discussion of the dynamics of the climate.
It does seem that I must learn more about climatic controls myself. Using one person's data and another's theory does not seem to work very well.

I am puzzled that you saw things in my last graph that I don't see there. I did not plot any coming months, and the last months I did plot do not show a rise in rainfall. Indeed, I am anxious to learn why the rainfall deficit simply became static last July, while the temperature peaked in November and then fell (if we trust unsmoothed values).

Here are a couple of charts I have made from my artificial neural network modelling:



These are for Cairns, on the north tropical Queensland coast, showing its monthly rainfall since January 1948 (the earliest date of record).

The top chart shows the actual rainfall received to December 2008 and the darker blue trace on the right is the projected rainfall from the model out to December 2013.

The lower chart is more of a 'closeup', showing the actual rain from January 2000, the projected rainfall as before, and, superimposed as a thick green trace, the actual rain from January to December 2009.

The projections in both plots are based on the entire data series. As is clear, the modelling for 2009 doesn't do well at all.

In the top chart, generally the projection seems to have detected not only the main summer cycles but smaller cycles as well. These are better seen in the lower chart.

So I don't really know what can be made of all of this. Certainly December 2009 would have been wildly off course! It may be that I have to include a cyclical component for extreme rainfalls.

The original rainfall data were not filtered to omit the 16 months or so over the entire range in which zero rain was recorded. I did try the modelling without those items but there wasn't much difference. So I thought it better to leave them there; after all, they aren't corrupt data but rather are a real component of the climate in winter in that area. A power transform of 1/12 was applied as this seemed to give the best correlation between the modelled rainfall and what actually fell (R^2 ~70%). There were 43 significantly correlated variables (out of 77). These 77 included factor scores derived from a principal components analysis on sea surface temperatures covering all available data on the NOAA site, divided into averages over 10 degree grids of all longitudes, out to latitudes 40°S and 50°N. I also included several standard ENSO variables and the average temperature, relative humidity, outgoing longwave radiation, surface pressure and precipitable water for each month of the period, for the Barron North Coast district, in which Cairns is located.

So I hope this doesn't scare people away! Food for thought at least, and constructive comments welcomed, of course.

Wow nice stuff Keith. Impressive to say the least. The data analysis sounds even more complicated than the actual ANN haha. An analyist in a past life perhaps? Agreed a bit of a way to go but its clearly showing seasonal variation. I would imagine monsoonal falls will be quite difficult to predict due to the unpredictable nature and timing of monsoonal events. Have you tried using seasonal forecasts from your model rather than monthly? Using ENSO climate indicators may make it difficult to increase the resolution of your model output because they are generally seen as seasonal forecasting parameters.

Thanks David, good point about the seasonality. I will try that and see what comes out in the wash.

I've been doing more work on these rainfall data, and this time decided I'd switch to Sydney (just for a change, and to see how it shaped up). This is a chart for one of Sydney's wettest suburbs:



In an earlier post I referred to using the Hodrick Prescott filter and decided to do that in this latest effort. The chart shows the original monthly rainfall data for Turramurra from January 1950. The dark blue wavy line is the trace of the HP filter using the recommended transform for monthly data.

The red line at the end is what the ANN predicted on the filter.

I know that HP filters are meant to extract trends from data series, so this is a prediction of a trend in the series.

Now, there's one problem: Is it feasible to invert the filtered data, then add back the residuals (the difference between the original and fitted data)? If so, how is the inversion performed?

I think I'm going about all this the wrong way somehow.

I didn't have much luck with seasonal data ie 3 months at a time but did try a 3 month moving average; probably that's where I'm going to end up with this.

Manilla Smoothed Monthly Anomalies of Climate Variables
Parametric Plots
Update for June 2010

New data for June 2010 allow updating with more smoothing applied to all months back to December 2009, which is now fully smoothed. A new commentary to replace the one in Post #866367 is not yet justified. New trends may be evident in two months time, when the summer months of 2009-10 will all be fully smoothed.




If you click the image you can get quite good resolution.

Just a very quick update on the analysis work I referred to a few posts back..if it's any update at all, it's that I cannot get these networks to make reliable step-ahead predictions. Although concurrent forecasts and historical data correlate well (>98% in some cases), the forecasting ahead is worse than atrocious.

Not sure if and when I will post on this aspect again..unless some form of new approach makes itself apparent.

I look forward to anything you can come up with, Keith. Predictions about the future are the most difficult kind.

Your station data was running close to mine in smoothed seasonal trends a while ago. Is it still doing that? Like the way there was a sharp temperature peak last spring, but scarcely a trough in the rainfall that you would notice.
I'm using a big Gaussian smoothing function (from Excel) on monthly values now, but the (1:2:1)/4 seasonal smoothing we both used before gives much the same result.
I had access to Wagga data a few months ago. A lot of the anomaly pattern is the same there, but some of it is different.

I'd have to revisit what I was doing at the time..but what is the Gaussian function you are using? Is it the NORMSINV or one of those similar ones (I'm not up to scratch on some of these Excel stats functions)?

And I'm not sure if what I am now going to try will be any use either, anyway I'm getting hold of some additional data that presumably have a direct effect on rainfalls. These include temperature, pressure/thickness values, humidity and wind at various heights (as well as the surface). I knew about these influences before of course, but there's so much data out there in so many different places with so many confusing descriptions (to the amateur) that I was put off from it for a while.

I am using one of NOAA's reanalysed datasets, but (and this is going to look funny, because it probably is), just now after getting over a dozen variables into a spreadsheet, ready to assault this issue with rigor and despatch, I noticed that the temperatures in July were higher than they were in December, at all levels.

Only one reason for this. Brain in gear..I had extracted the northern hemisphere's data and not the southern. Reason: I didn't put a minus sign in front of the latitude coordinates! Confused by online instructions? Probably, but I have to admit fault..just didn't read them carefully enough.

So work is progressing. Predict or perish.

I think I have it.

Here is the result of many further long hours of rainfall modelling:



I was going to start a new thread but as I had already posted on it here I thought this is where I should continue. So now for a bit of explanation.

The chart is the result of 2 different models:a neural network and a regression model.It covers monthly rainfall from June 1950 to May 2009, with projections to December 2012.

The data itself is the average of the monthly rainfalls of certain locations in western and eastern Sydney, the upper and lower central tablelands, the Hunter district and the mid-north coast (all in NSW, of course).

Locations within each of these districts were restricted to those which had a minimum of 95% of the months with data. Others were discarded. Before modelling, a power transform of 1/12 was applied. This was mainly to force the neural network to a higher correlation with the original rainfalls. For consistency the same transform was used in the regression model. In all cases the predicted rainfalls were back-transformed by inversion of the original.

The motivation for modelling was that it was clear that rainfalls are dependent on a highly complex mix of climatic variables. This varies from one location to another. In the end I considered 245 different independent variables, including sea surface temperature anomalies in 2.5° blocks of latitude from 50°N to 40°S, across all longitudes. There were also things like wind strength, humidity, specific humidity, temperatures and precipitable water values from the surface upward to the 500mb constant pressure level. In the end, this narrowed down to just 10 variables. I'll skip detailed statistical comment (whew! thank goodness for that, I hear you say).

The chart shows the original average rainfall across the 4 climate districts and the historical rainfall predicted by the models for the same period. I have only graphed from January 2000 to make it easier to compare. The faint dotted lines on the right of the chart are the limits within which we would have 95% confidence of the forecast rainfall occurring (as measured by the regression model). In other words we would be 95% confident that the forecast rainfall would lie between the two dotted lines (as read off from the vertical axis on the left). You will notice that most of the neural network model rainfall projection falls within these confidence intervals, indicating that by and large, the models seem to be at least reasonably useful.

The 2 models performed rather differently to each other and I thought that a reasonable guess could be had by averaging them. This is what the bright red line represents. Individual model projections are also shown. The neural model seemed to be much more sensitive to extreme rainfalls than the regression model.

The models seem to suggest that they have predicted the occurrence of La Nina in the latter half of 2010, with very wet weather expected in November. I found however that individual ENSO predictors such as NINO34 and NINO4 were insignificant in the regression model. It may be that it has failed to take account of those variables. It would be instructive to rerun the whole thing from a more recent time, say 1980, and compare results. However I've not done that at this stage as this has been more of a learning exercise so far.

I hope people find this of interest (and not too heavy!).

Keith, your graph appears as just a little icon which won't make an image for me. Maybe it is just my tired computer.

Hmm, don't know why that is. It's showing up here large as life.

I have an image now. So you forecast no droughts in coastal NSW before 2013?

On the face of it, yes, no droughts there. I'm going to rerun the whole process using a Monte-Carlo simulation of the data to see how the modelling works with the equivalent of say 300+ years of observations. I've seen this done in several academic papers I've read.

I had mentioned going just from a more recent date but a neural network model will be less accurate. The more data it gets, the better..apparently.

I am generally of the view that the current drought will be further reduced but that doesn't mean of course that some areas will get dry spells in between the wet ones.

Should be '..won't get..' etc.

Every drip has a climate story to tell

WALKING into a cave of stalagmites and stalactites is like entering a Tiffany's showroom. But unlike an expensive ring, the sparkle of these formations is a precious record of climate history.

Australian scientists are using structures from the Wombeyan Caves, south-west of Sydney, to build a picture of rainfall patterns in NSW over the past 1000 years.

They reveal the area has been subject to an increasing drying trend since around the 1600s, she said.

The drought which has engulfed south-east NSW for the past decade or so now appears to be part of a larger drying period, said Dr McDonald, who presented her findings at the Australian Earth Sciences Convention conducted by the Geological Society of Australia in Canberra last week.

The historic rainfall data will be used in forecast models to better predict the area's climate cycles.

http://www.smh.com.au/environment/climat...0711-105pv.html

Just a quick update..this hasn't worked as I think it needs to capture the particular statistical qualities of the data (ie not just the distribution curve, average and standard deviation). It seems to me that it's incapable of duplicating the effect of cycles and other variables.

So, still in front of the 'drawing board'...

Cloudiness

Cloudiness at Manilla NSW has changed dramatically during the last twelve years. Data comes from observations of total cloud in Octas at about 9 am daily. More than four octas is counted as a "Cloudy morning" and is summarised as a percentage for each month. The mean pattern in the 10 years from June 1999 is:
Jan: 26.2%
Feb: 30.6%
Mar: 24.1%
Apr: 29.6%
May: 24.4%
Jun: 33.5%
Jul: 35.3%
Aug: 30.2%
Sep: 23.8%
Oct: 27.8%
Nov: 33.4%
Dec: 31.1%
I have subtracted these normal monthly values to get the anomaly values analysed here.


The first graph shows monthly cloudiness anomaly values and (in purple) values smoothed with a gaussian smoothing function of half-width 6 months.
The pattern is complex, and not easily described. It is clarified by using CUSUM analysis.
CUSUM analysis was invented in 1954 by E. S. Page, to allow early detection of changes in trends, such as those caused by damage to a die in an automatic machine. The data for a CUSUM sequential analysis is simply the sum of all values to a particular time.


The second graph is a CUSUM plot for this data set. There is an abrupt, very large change in the trend in mid-2007. I fitted the linear trends accurately by using the Excel Charts Trendline function on separate plots for data from 1999 to 2007 and for 2007 to 2010. The first trend line has a slope of -2.46 units per month, and the second a slope of +9.08 units per month.
Only the slopes of these trend lines, and their point of intersection in August 2007 are of interest. A CUSUM trend line of constant slope represents a constant mean line for the original data above (+) or below (-) the longer-term mean.


In the third graph I have inserted the two mean value lines that correspond to the two CUSUM trend lines. This shows the abrupt increase in mean cloudiness in August 2007 by 11.5 percentage units.
This is a very large change. Certain monthly anomaly values lie on the first mean line. The actual percentage cloudiness for those points are near 30.5%, making cloudiness near the second mean line about 42% (i.e. 30.5% + 11.5%). Expressed as numbers of cloudy mornings per month, the once typical value of nine has been replaced by twelve: an increase of one third.

Having established the two mean lines that reflect the trend lines from the CUSUM plot, I subtract them to get the de-trended cloudiness anomalies shown in the fourth graph.

The smoothed values (purple) now consistently oscillate about the zero line. The period and amplitude of oscillation is strikingly different before and after mid-2007. Counting peaks and troughs in the curve gives estimates of period of 20.5 months before mid-2007 and 8.4 months afterwards. The amplitude of oscillation changes from about 5 units to about 1.5.

So far as cloudiness at Manilla NSW is a climate indicator, there was a major change in August 2007, give or take a month or so, involving a 30% increase in the amount of cloud, and such a reduction in the amplitude and period of oscillation that Quasi-Biennial Oscillation essentially ceased.

I have been working further on the rainfall modelling I posted on earlier in this thread and have started a seperate thread here.

Manilla Smoothed Monthly Anomalies of Climate Variables
Parametric Plots
Update for July 2010

New data for July 2010 allow updating with more smoothing applied to all months back to January 2010, which is now fully smoothed. A new commentary on the trends to replace the one in Post #866367 is not yet justified. New trends may be evident in a months time, when the summer months of 2009-10 will all be fully smoothed.

The July 2010 data, marked with orange diamonds, shows an extraordinarily moist month. Anomaly values of rainfall, cloudy days, Dew Point, temperature range and minimum temperature are so extreme that the graph margins have had to be moved.
Extreme moisture is usually associated with low maximum daily temperatures, in the "Flooding Rains" corner of the graphs, but this July the maximum temperature is not extremely low, but normal.


Sorry, the red square which should mark January 2010 is on December 2009 in the top left graph.

Manilla Smoothed Monthly Anomalies of Climate Variables
Parametric Plots
Update for August 2010

This updated set of parametric plots shows that Manilla's recent climate continues to be marked mainly by cloudier skies and a narrower daily temperature range than in the 12-year averages.
In the current plots we see final smoothed trends for summer 2009-10, plotted as filled red diamonds. Unfilled diamonds mark provisional trends for autumn of 2010 and little-smoothed to unsmoothed points for winter 2010, ending with an orange diamond for August 2010.

I last discussed these trends in June (Post #866367).

Daily Maximum Temperature
On each plot, the x-axis has the smoothed anomaly of mean daily maximum temperature. The min value in Feb 2008 (-1.61) and max value in Nov 2009 (+1.35) are also the extremes (plotted in blue) of the smoothed data set. In summer 2009-10 the smoothed temperature anomaly fell much faster than it had risen in the previous winter and spring. During autumn the max temp anomaly was near zero, and the raw value for August is so low (cold) the scale has had to be extended.

Monthly Total Rainfall (y-axis (inverted), first graph)
The rainfall anomaly showed a very mild drought throughout the last winter, spring, and summer. Autumn seems to have been less droughty, much like the previous autumn, but with the opposite trend. July 2010 was very wet, but August rainfall was normal.


Cloudy Mornings %
During winter 2009, the positive cloudiness anomaly fell as the max temp anomaly rose. During summer 2009-10 it rose again as the max temp anomaly fell. However, for a given max temp anomaly the cloudiness anomaly was now more positive (it was cloudier). Since the end of summer, skies have been extremely cloudy (See Post #874468).



Early Morning Dew Point
The final year's data on this graph plots like that of the previous graph. However, Dew Point anomalies were not so positive: in winter and spring 2009 they were quite strongly negative. By autumn 2010, Dew Points seem normal, and recent values are high (humid climate).


Daily Temperature Range
Again, the last year's pattern on this graph is like that on the previous two. This time values low on the graph are negative anomalies, representing narrow daily temperature ranges. For a given max temp anomaly, the anomaly of daily temperature range was lower during the summer than it had been the previous winter. Fully smoothed temperature range anomalies for autumn 2010 seem likely to be low enough to match the minimum of the smoothed record (blue), and the raw values this winter have been even lower (equable climate).
(Note that, despite the label in the corner of the graph, this August's max temp (16.8 degrees) is actually much higher than that of Macquarie Island (5.0 degrees), and the daily temperature range (11.9 degrees) is still much wider than that of Macquarie Island (3.5 degrees).)


Daily Minimum Temperature
Summer 2010 began with a record maximal value of smoothed daily minimum temperature anomaly, and the value stayed high through the season. Values may have been lower in autumn, then they seem to have risen even higher (very warm nights).


Subsoil Temperature
Subsoil temperature anomalies have remained close to zero for 17 months, despite big changes in the anomalies of other temperatures.

Nearly all nights now much warmer at Manilla NSW
New Manilla data show that nearly all nights of the year have become much warmer during the last nine years.

Graphs like these (but one year older) are introduced in Post #3785 on Page 3 of this thread. All the days (or nights) of the year are ranked from the coldest on the left to the hottest on the right. Columns show whether that day or night has tended warmer or cooler during the nine years. Season labels, and the temperature boundaries between them are not stricly correct. It is well known that the hottest days of 2009 were in spring, not summer.
In today’s graphs, blue columns show the temperature trends for the earlier nine-year period to August 2009, and red columns for the nine-year period to August 2010.

Graph No. 1 shows trends in daily maximum temperature (“Days”). In the earlier period the strongest trend had been to cooler summer days, but this trend has now ceased. A trend to cooler winter days in the earlier period has now expanded to the whole of the cooler half of the year. Trends to warmer days are grouped at the boudary between summer and spring or autumn. This pattern returns to one seen in the period to August 2008. Over all, in the latest 9-year period, days have been getting cooler at the rate of -0.0287°/yr.
Graph No. 2 shows trends in daily minimum temperature (“Nights”). The new curve is almost the same shape as the earlier one, but each trend has moved in the positive direction. Just a few of the hottest nights are still getting cooler, but all other nights have been warming rapidly. The over-all average rate for night-time warming is +0.1506°/yr.
Taking days and nights together, the mean temperature rose by +0.0609°/yr, about six times the Australian 100-year mean rate of warming. However, the difference between days and nights – the daily temperature range – is a more striking result: it narrowed by -0.1792°/yr. Over the nine years the linear trend of the mean daily temperature range fell by an astonishing 1.6°, from 16.2° to 14.6°.
This narrowing of the daily temperature range at Manilla in the last decade agrees with the sharp increase in cloudy days noted in Post #874468.

The published graphs and trends for Australian climate change show that narrowing of the daily temperature range has been nearly as great as the rise in mean temperature during the last 100 years. Daily minimum temperature has risen much faster than daily maximum temperature in all states except Victoria and Western Australia.

Manilla Smoothed Monthly Anomalies of Climate Variables
Parametric Plots
Update for September 2010: "Flooding Rains".

New data for September 2010 allow updating with more smoothing applied to all months back to March 2010, which is now fully smoothed. A new commentary on the trends to replace the one in Post #880243 will be held over for two months, when the autumn months of 2010 will all be fully smoothed.

The September 2010 data, marked with orange diamonds, are in the "Flooding Rains" area in the bottom left corner of five of the six graphs. Anomaly values of rainfall, cloudy days, Dew Point, temperature range and subsoil temperature are extreme. The Minimum temperature anomaly has remained very high for a year.

Warmer nights at Manilla

This post adds to Post #884837
which presented the daily temperature trends for every day of the year during the 9-year period to August 2010

The smaller graphs here show Manilla's average figures year by year, with linear trend lines. (Years begin in September before the labelled year, and end in August.)

The year “ ’08” was very cold, by day and by night. It was remarkably cold in December 2007 and January, February, March, April and August 2008. It was as if Manilla had moved 400 km south or 150 metres higher.
Apart from the year “ ’08”, the trends are rather steady. Days hardly warmed or cooled at all at Manilla in these nine years, but nights warmed a lot. (Affecting electric blanket sales, I wonder?)
These trends will soon change. However, they reflect long-term climate trends that should be better known.
See these Bureau of Meteorology charts. (Set the running average slider to “T”. The trend-line slope is in tiny print below the graph.) For Australia during the last 100 years the warming trend of daily maximum temperatures has been only 0.08°/decade, but the warming trend of daily minimum temperatures has been 0.12°/decade. Nights have warmed 50% faster than days. For NSW the figures are 0.05°/decade and 0.11°/decade: nights have warmed more than twice as fast as days.

It is as if we are all moving down to the coast.

Cycles of 30 months, 20 months, and 8.6 months

The Quasi-Biennial Oscillation has appeared frequently in this thread. See discussion in Post #863801

The period of the QBO, as estimated using fast fourier transforms is roughly 30 months. I originally estimated the period of oscillations in my data, and in BoM long-term monthly temperature and rainfall data, as about 20 months. I did this by inspection of plots of smoothed monthly time series.
I thought that the shorter period I observed was probably an artifact, as the eye tends to see the shortest periods in an oscillating time series (e.g. in river meanders).
Later, in a Post on cloudiness I estimated that a periodicity of 20.5 months was replaced suddenly in August 2007 by one of 8.4 months.

It seems that these three periodicities may be real after all.
In a 1994 paper, M.P.Baldwin and Ka Kit Tung find that the equatorial stratospheric 30-month QBO interacts with annual oscillations to form stable harmonics of period 20 months and 8.6 months in extra-tropical areas.


This topic clearly fits within the Forum "Climate and Climate Change" but there is no suitable thread there.

Some good work there Surly. To have warm nights increasing faster than cooler days is what you would expect if the climate was on a warming trend.
You do not do any grass readings by any chance?.
One thing that fascinates me are grass readings compared to the screen readings. I have long believed that if CO2 was to have the expected result on the climate then there should be a measurable change in the differences between grass and screen. The differences should get smaller given the same humidity. Unfortunately this is nearly impossible to prove because there are no decent historical records of grass readings.

No, Snowmi, I have no grass readings.
Actually, I don't have a lawn or a lawnmower!

Why would you expect CO2 to relate to grass/screen difference?

You do not need a lawn to take a grass reading, just the surface of the Earth in whatever form you have will do, gravel, dirt, the term "grass" just refers to the level above the surface that is within 1-2cm. By measuring the temperature at the exact interface of the Earth surface and the atmosphere you are measuring what effect the Earth surface is having on the heating or cooling of the atmosphere. The solid Earth surface is what is heating and cooling the atmosphere over the continents. Without the continents to absorb the Suns rays the Earth would turn into an Europa ice ball and without the oceans the Earth would swing from +100 to -100deg C between winter and summer.

As the sun sets the Earth immediately and rapidly starts to emit the excess IR back into space and you suddenly get a very rapid drop in temps right at the surface (grass level). The rate and amount of this drop in temperature is controlled by the amount of greenhouse gasses, if it is more humid the drop will be less and also in theory if there is more CO2 then the drop in temperature at the very surface should also be a little less.

If I could look at detailed records of humidity and temperature of two sensors one at screen level and one at grass level over the last 50 years then if CO2 does what its suppose to do then there would have to be a measurable change in the differences between screen and grass. Eg I would expect an average difference to change by say 0.5deg during lower humidity's from say 5.5deg difference to 5.0deg difference at 30% RH. The difference being caused by a change in CO2 from 300ppm to 390ppm. But as far as I know no one has made any accurate measurements like this 50 years ago.

Grass temps are a constant puzzle for me which is why I have contributed a fair bit to this thread.
http://forum.weatherzone.com.au/ubbthreads.php/topics/57739/3

If CO2 was involved it could hardly make any real difference given its fraction ..very small.. of the total atmospheric pressure. In any case the grass would continue to absorb CO2 at the rate it always does, wouldn't it? If it absorbed more due to more CO2 then that's a good thing (for AGW supporters) and probably not a bad thing anyway.

The standard (I think worldwide) criterion for measurement of minimum temperatures on the ground (labelled 'terrestrial') is that the thermometer should be at the top of the tips of grass blades..obviously not long ones. I guess if one wanted to do a more elaborate study one could indeed measure temperatures over gravel or other surfaces.

It's true that the rate of drop of the temperature is controlled by moisture in the air, but also by wind and cloud cover, amount and depth, and I guess whether there is a lot of visible pollution like dust or smoke. I can't see how CO2 could possibly affect it, short of suffocating the whole human race.

I am not so sure that the effect would be negligible. It is very difficult to estimate though, but lets say that at 100% humidity there is 1% of the total weight of the air as water. However at this RH the grass temp will be the same as the air temp. So lets lower the RH to 70% to start to get some ground cooling. Also however CO2 takes up 3% of the effect of water at 300ppm and 4% of the effect of water at 400ppm (since that .04% is 4% of the 1% of water). So then you are comparing an RH of 70% or 74% on the grass temperature and that could easily be a measurable difference of close to 0.5deg. Even these calculations are not quite right as the air thins and holds less relative moisture as you rise through the column of air above until you reach space where the IR is dissipated. The amount of CO2 increase in the last 50 years is almost the same as like adding 1% RH to all the humidity's worldwide that could have a measurable effect on radiation loss from the surface.

Manilla Smoothed Monthly Anomalies of Climate Variables
Parametric Plots
Update for October 2010: "More Flooding Rains".

New data for October 2010 allow updating with more smoothing applied to all months back to April 2010, which is now fully smoothed. A new commentary on the trends to replace the one in Post #880243 will be held over for another month, when the autumn months of 2010 will all be fully smoothed.

The October 2010 data, marked with orange diamonds, are again in the "Flooding Rains" area in the bottom left corner of five of the six graphs. Anomaly values of rainfall, cloudy days, Dew Point, temperature range and subsoil temperature are extreme. The Minimum temperature anomaly is now near zero but falling rapidly.

In the post above, an extreme value I forgot to mention was the daily maximum temperature anomaly, plotted on the x-axis of all six graphs.
The Manilla mean daily maximum temperature anomaly in October 2010 was nearly three degrees below the average for the decade 1999 to 2008.

Log of Smoothed Maximum Temperature and Rainfall
Last 3 years at Manilla

In Post #869352 of 24/6/10 I presented an earlier graph like this, showing Manilla's smoothed (gaussian, half-width 6 months) monthly temperature and rainfall anomalies. This one has complete smoothing to May 2010, with reduced smoothing for later months, until November 2010 is the raw anomaly (provisional to 28/11/10).


Recently, the rainfall anomaly curve has tracked the daily maximum temperature curve fairly closely, with each 12 mm of positive rainfall anomaly matching minus one degree of temperature anomaly. The last positive temperature anomaly peak in November 2009 was sharp, but the corresponding negative rainfall anomaly peak was broad.
Currently there is a large negative anomaly of maximum temperature and a large positive anomaly of rainfall. However, future smoothed values may not be quite so large.

As amendment to the above graph in Post #901622, final rainfall data make the November 2010 raw rainfall anomaly value +38.3 mm rather than +29.9 mm.

Manilla Smoothed Monthly Anomalies of Climate Variables
Parametric Plots
Update for November 2010

I last discussed these trends in September (Post #880243).
In the current plots we see final smoothed trends for autumn 2010. These are plotted as filled red diamonds, the last being for May 2010. Unfilled diamonds mark provisional trends for winter 2010 and little-smoothed to unsmoothed points for spring 2010, ending with an orange diamond for the November 2010 raw value.

Daily Maximum Temperature
On each plot, the x-axis has the anomaly of mean daily maximum temperature. The min value in Feb 2008 (-1.61) and max value in Nov 2009 (+1.35) are also the extremes (plotted in blue) of the smoothed data set. In autumn 2010 (ending May-10) the smoothed temperature anomaly fell much faster than it had risen in the previous autumn. Partially smoothed later values suggest that an extreme negative temperature anomaly came in October 2010.

Rainfall
Rainfall anomalies are plotted (inverted) on the y-axis of the first graph. Fully-smoothed autumn values rose with falling maximum temperature, but remained negative. Later values generally rose rapidly to an extreme positive raw value of +38 mm in November.



Cloudiness
During autumn of 2010 the anomaly of percent cloudy days rose to a new record for fully-smoothed values. It continued to rise to an apparent extreme in October 2010.


Early morning Dew Point
The Dew Point anomaly was positive and rising in autumn. It seems to have peaked at less than a record value in September 2010.


Daily Temperature Range
The temperature range anomaly fell during autumn to a new negative record for smoothed data. It continued to fall to an apparent negative peak in September 2010.



Daily minimum temperature
Autumn daily minimum anomalies fell little below the peak of December 2009. Later they traced an arc through the "Equable" zone of the graph, ending near zero.



Subsoil temperature
The autumn subsoil temperature anomaly was near zero and scarcely falling. It then accelerated downwards to an apparent negative extreme in October 2010.

This is a big climatic swing that we are having.


For over a year, there has been a persistent rapid decline in daily maximum temperature, matching a rapid rise in rainfall.
The swing seems to amount to about four degrees in temperature and 55 mm in monthly rainfall. If the peaks have already been reached, the swing will become rather less when the latest data have been smoothed.
Still, it will be much bigger than any other swing in this twelve-year record.

Most anomalies on this record are small: less than 20 mm per month for rainfall and one degree for temperature. Current anomalies are twice as large.
Other large anomalies are:
1. The 2002 extreme drought, with anomalies peaking in autumn at -26 mm for rainfall and +1.3 degrees for temperature;
2. Excessive rainfall (noticed by nobody) in spring 2005, peaking at +20 mm, with no temperature anomaly;
3. A very cool summer in 2007-08, with temperature anomaly -1.6 degrees, but no rainfall anomaly;
4. A heat-wave in spring 2009, with temperature anomaly +1.3 degrees, just as in the 2002 drought. This time, however, the rainfall anomaly was much less: only -15 mm.

Manilla Smoothed Monthly Anomalies of Climate Variables
Parametric Plots
Update for December 2010: "Still More Flooding Rains".

New data for December 2010 allow updating with more smoothing applied to all months back to June 2010, which is now fully smoothed. A new commentary on the trends to replace the one in Post #902913 will be held over for another two months, when the winter months of 2010 will all be fully smoothed.

The December 2010 data, marked with orange diamonds, are yet again in the "Flooding Rains" area in the bottom left corner of five of the six graphs. Anomaly values of rainfall, cloudy days, Dew Point, temperature range and subsoil temperature are extreme. The Minimum temperature anomaly is stuck near zero.

Manilla Smoothed Monthly Anomalies of Climate Variables
Parametric Plots
Update for January 2011: "Flooding Rains" stop.

New data for January 2011 allow updating with more smoothing applied to all months back to July 2010, which is now fully smoothed. A new commentary on the trends to replace the one in Post #902913 will be held over for another month, when the winter months of 2010 will all be fully smoothed.

The January 2011 data, marked with orange diamonds, have leapt away from the "Flooding Rains" area in the bottom left corner of the graphs. Anomaly values of Maximum temperature and temperature range are up, and that of rainfall is down.

Moisture from the seas north NW and NE of northern teritory are feeding moisture into the remnants of yasi. The system is gaining size. and looking to grow its tail is nearly connecting with the southern system in east SA and all of Vic Why is it starting to spiral. Is there such a thing as an inland cyclone or is that just a pretty pattern of spiralling cloud?. Refer to your satellite image on this page

Manilla Smoothed Monthly Anomalies of Climate Variables
Parametric Plots
Update for February 2011

Sudden Rise in Temperatures!

I last discussed these trends in December (Post #902913).

In the current plots we see final smoothed trends for winter 2010. These are plotted as filled red diamonds, the last being for August 2010. Unfilled diamonds mark provisional trends for spring 2010 and little-smoothed points for summer 2010-11, ending with an orange diamond for the February 2010 raw value.

Daily Maximum Temperature
On each plot, the x-axis has the anomaly of mean daily maximum temperature. The min value in Feb 2008 (-1.61) and max value in Nov 2009 (+1.35) are also the extremes (plotted in blue) of the smoothed data set. In winter 2010 the smoothed temperature anomaly fell very rapidly to near the earlier record low smoothed value. Partially smoothed later values suggest that an extreme negative temperature anomaly came in November 2010. The raw value for February 2011 is remarkably high.

Rainfall
Rainfall anomalies are plotted (inverted) on the y-axis of the first graph. Fully-smoothed winter values were all positive, rising with falling maximum temperature. Later values generally rose rapidly to an apparent positive extreme in November.February's value was very low and January's was very high.


Cloudiness
During winter of 2010 the anomaly of percent cloudy days rose to yet a new record for fully-smoothed values. It continued to rise to an apparent extreme in October 2010, since when it has remained extremely high.


Early morning Dew Point
The Dew Point anomaly was positive and rapidly rising in winter. It seems to have peaked at less than a record value in October 2010. By February the anomaly was much less positive.


Daily Temperature Range
The temperature range anomaly fell during winter to yet a new negative record for smoothed data. It continued to fall to an apparent negative peak in October 2010. The raw value for February and the little-smoothed value for January are right back near normal.


Daily minimum temperature
Winter daily minimum anomalies remained little below the peak of December 2009. Later values traced an arc through the "Equable" zone of the graph, until February's raw value was again near the December 2009 peak ("Hot Days; Hot Nights").


Subsoil temperature
The winter subsoil temperature anomaly was below zero and falling quite rapidly. It then accelerated downwards to an apparent negative extreme in November 2010. By February, the raw value was back higher than any point on the 3-year trace.

Manilla Smoothed Monthly Anomalies of Climate Variables
Parametric Plots
Update for March 2011: Extreme cloudiness.

New data for March 2011 allow updating with more smoothing applied to all months back to September 2010, which is now fully smoothed. Smoothed September values include new 12-year record anomalies for daily maximum temperature (low), cloudy days (high) and daily temperature range (low).
Most of the March 2011 raw data values (orange diamonds) are nearer to normal than to recent extremes. As an exception, the March cloudiness anomaly is a record +37%: there were 19 cloudy mornings instead of 8.



A new commentary on the trends to replace the one in Post #969272 (above) will be held over until the spring months of 2010 are all fully smoothed.

Manilla Smoothed Monthly Anomalies of Climate Variables
Parametric Plots
Update for April 2011: Sunny return from "flooding rains".

New data for April 2011 allow updating with more smoothing applied to all months back to October 2010, which is now fully smoothed. Smoothed October values include new 12-year record anomalies for daily maximum temperature (low) and cloudy days (high).
Most of the April 2011 raw data values (orange diamonds), other than daily maximum temperature, are now on the "droughts" side of normal rather than the "flooding rains" side, which had dominated for about a year.



A new commentary on the trends to replace the one in Post #969272 (second above) will be held over until the spring months of 2010 are all fully smoothed.

"Flooding rains" climate peaked here in October 2010
Manilla Smoothed Monthly Anomalies of Climate Variables
Parametric Plots
Update for May 2011

Smoothed data to November 2010
Fully smoothed data for spring of 2010 show that several variables reached peak values during the season. First, temperature range anomaly reached a minimum in September. Next, in October, daily maximum temperature anomaly reached a minimum, rainfall anomaly a maximum, and Dew Point anomaly a maximum. In November, subsoil temperature anomaly reached a minimum. Cloudiness anomaly may have reached a maximum in that month, but perhaps smoothed December or January values will be higher.
Daily minimum temperature anomaly did not peak. Through spring it fell steadily from a high positive value.

In an earlier post I noted a tendency for variables to peak in a particular order.
"For a "flooding-rains" peak:
First: Daily Temperature Range (min);
One month later: Rainfall (max), Cloud (max);
Two months later: Daily Max Temperature (min), Dew Point (max);
Four months later: Daily Min Temperature (min), Subsoil Temperature (min)."

Not much is different in this case. In particular, daily temperature range was the first to peak (WHY?), and subsoil among the last. However, the time of rainfall maximum did not precede the time of daily maximum temperature minimum.



Data after November 2010
During summer most variables seemed to be moving towards "droughts" but this pattern broke down in autumn:
Max temp anomaly did not quite reach normal before falling again;
Rainfall anomaly became negative then returned to positive;
Cloud anomaly had just one negative value before returning to record cloudiness;
Dew Point anomalies accelerated to extremely low values;
Temp range anomaly went very positive before falling slightly;
Min temp anomaly fell rapidly, independent of max temp anomaly;
Subsoil temp anomaly stabilised at a slightly positive value.

I am surprised that extreme cloudiness and extreme low humidity occur together. I am also puzzled about the weak association between daily max and min temperatures. This is a worry because daily minimum temperatures provide the most consistent signal of climate change during the last century.

Note:
New data for May 2011 allow updating with more smoothing applied to all months back to November 2010, which is now fully smoothed. Fully smoothed data - gaussian smoothing with half-width 6 months - are plotted in red, partly smoothed data uncoloured, and unsmoothed data for the last data point in orange. January data points are marked by squares.

No droughts or floods here lately
Some parts of Australia have had floods or droughts lately. Rainfall records show it is NOT true for the north-west slopes of NSW. Perhaps there are other areas for which the same can be said.

To take long duration effects first, 30-year (360-month) rainfall totals have been neither extremely high (>90th percentile) nor extremely low (<20th percentile) since 1999-2000, when they were extremely high. They were similarly high in 1991 and 1984, and phenomenally high (>99th percentile) in 1978-79. The last time of extremely low rainfall of 30-year duration was 1954, more than half a century ago.
For ten-year (120-month) rainfall totals, the year 2010 had extremely low values due to lingering effects of the brief (in this area) drought of winter 2002. By 2011 this effect had passed. Going back, the next extreme values were high values in 1984, then phenomenally high values (as above) in 1978-79. Before 2010, the most recent extremely low 10-year rainfalls were in 1966-67.
For 12-month rainfall totals, extreme values come more frequently. There were extremely low values from November 2009 to June 2010, in January and February 2007, and (the 2002 drought) from July 2002 to April 2003. The latest extremely high 12-month values were August 1998 to April 1999: a high-rainfall event almost exactly compensating for the following low-rainfall event of 2002.

Rainfall events that are extremely high (leading to floods) or extremely low (droughts) lend themselves to hyperbole (now shortened to a buzzword). The weather really has been quite close to the average in recent years, at least here, providing nothing for demagogues to get their teeth into.

Dry air but not warm or sunny

Manilla Smoothed Monthly Anomalies of Climate Variables
Parametric Plots
Update for June 2011


Raw anomaly data for June 2011 (shown in orange) are a little strange.
Daily max temperature, shown on the x-axis of all six graphs, has stalled without quite rising to normal from the extreme cold of last October.
Two variables indicate severe drought: Rainfall was very low, and so was the early morning Dew Point.
Most other variables are near normal, or slightly to the "flooding rains" side of normal.
Percent of cloudy mornings (>4 Octas) remains stable at a very high positive anomaly. For a calendar month that had 35% cloudy mornings in the reference decade beginning in 1999, it now has 55% cloudy mornings. (Has this really happened nowhere else? Is Manilla uniquely affected?)

Note:
New data for June 2011 allow updating with more smoothing applied to all months back to December 2010, which is now fully smoothed. Fully smoothed data - gaussian smoothing with half-width 6 months - are plotted in red, partly smoothed data uncoloured, and unsmoothed data for the last data point in orange. January data points are marked by squares.

Peaks and troughs of temperature in the Quasi-Biennial Oscillation
Manilla, NSW data from 1999

Most of this data was graphed in an earlier post.
Fully-smoothed data (gaussian smoothing with half-width six months) now extends to January 2011, with partially-smoothed data for six months after that.

The graph shows anomalies relative to (1999-2008) monthly averages of daily maximum temperature. After smoothing, the pattern is a Quasi-Biennial Oscillation (QBO) with clear peaks and troughs, as marked. The amplitude of the oscillation was less than half a degree in 2005-6, but approached minus two degrees in October 2010. Daily maximum temperature has not returned to normal since that time
Counting only the marked peaks and troughs, the mean period of oscillation during 139 months was 18.5 months. The shortest period was 8 months (May-05 to Jan-06) and the longest 35 months (Dec-06 to Nov-09).
In this data set, certain seasons have more peaks and troughs than others:
Summer (DJF): 4/15;
Autumn (MAM): 3/15;
Winter (JJA): 1/15;
Spring (SON): 7/15.
Nearly half of the peaks or troughs occurred in spring. Whether or not spring is generally the preferred season for a peak or trough in the QBO, it is true that spring has been preferred lately.

My data is local. However, there must be some wider area where results would be similar. I suggest that this pattern may apply to the region "Rain1" in Fig. 2 of Drosdowsky and Chambers (1998).
Region "Rain1" is centred near Dubbo, NSW, (close to here) and is bounded by a line through Brisbane, Cunnamulla, Hillston and Bega.

These results must surely relate to discussions about the influence of Sea Surface Temperatures on Australian climate.

I am not qualified to comment, but I hope others may do so. To say that one peak or another matches an ENSO event will not do. The whole pattern needs to fit.

Peaks and troughs of rainfall in the Quasi-Biennial Oscillation
Manilla, NSW data from 1999

This graph shows rainfall anomalies rather than the daily maximum temperature anomalies in the post above. I thought I should present this because the research that defined the Australian climate region "Rain1" analysed rainfall, not temperature.
Anomaly values are relative to monthly means of the 125-year record.
As shown in various posts on this thread, peaks and troughs in smoothed rainfall anomaly tend to coincide with, or slightly lead those of maximum temperature taken in the opposite sense: high temperature with low rainfall. However, there are many mis-matches.

Analysis of this graph finds it very similar to the temperature graph:
Counting only the marked peaks and troughs, the mean period of oscillation during 139 months was 14.5 months. The shortest period was 7 months (Sep-07 to Apr-08) and the longest 29 months (Nov-00 to Apr-03).
In this data set, certain seasons have more peaks and troughs than others:
Summer (DJF): 2/19;
Autumn (MAM): 6/19;
Winter (JJA): 2/19;
Spring (SON): 9/19.
Again, nearly half of the peaks or troughs occurred in spring. Whether or not spring is generally the preferred season for a peak or trough in the rainfall QBO, it is true that spring has been preferred lately.
The raw rainfall anomaly value for July 2011 plots off the bottom of the graph. It will not be clear whether the current rainfall deficit is worse than that of 2002 for another six months at least.


As before, I invite anyone with detailed knowledge of the history of sea surface temperatures or the Southern Oscillation Index to comment on how this specific pattern of anomalies reflects that history.

Surly, that link to Drosdowski in your penultimate post is most useful. I'm going to have a go at reproducing the process it describes, in relation to rainfall and the SSTs...although I've not yet worked out whether I can take it further and if so, how.

SB, Climate4you has an extensive post including sat based graphs and etc on global cloud cover changes which might be of interest and relevance to you.

Keith
I am glad the you may be able to build on the link to Drosdowsky and Saunders' 1998 paper.
I am just returning the favour, as you alerted me to Drosdowsky's 1993 papers. I made serious attempts to get more information about Drosdowsky's work or updates of it, but I failed. I infer that Australian government research on climate on seasonal time-scales ceased about the turn of the century.

ROM
Many thanks for that link to the "climate4you" web-site of Professor Ole Humlum (University of Oslo). That site is invaluable for comprehensive up-to date data on world climate.
I particularly appreciated his demonstration of the very poor fit of global warming and atmospheric carbon dioxide concentration.
Unfortunately, the cloud data set you linked ends in 2008, just when my cloudiness data goes ballistic. It does show that my base period is in a time of low global cloud cover, much lower than the 1980's. Perhaps we have returned to amounts of cloud cover that were normal then.

You're right SB. All the global cloud cover data seems to stop in 2008.
Bob Tisdale [ Climate Observations ] suggested KNMI Climate Explorer and I know that a lot of the techo climate bloggers are into KNMI for a lot of climate data but the cloud data stops in 2008.

Likewise with "The International Satellite Cloud Climatology Project (ISCCP) "
And the the online data. It just stops in 2008

OK another try at these clouds. CALIPSO Cloud Sat [ data ]
Home; Cloud Sat CALIPSO

PATMOS-x Cloud Cover, Layers, Temperatures and Emissivities


Maybe of interest; Klein Cloud Trends poster [ PDF ]