January 2016: moist again

Photo of Senna-bush

Jewel-box Cassia

Unlike December, this month saw varying temperatures. The week around the 7th was quite cool. The maximum on the 3rd reached only 22.3°, but worse was to come. The 15th reached only 21.1°: equal coldest January day in the new century (with 31/1/2001). It was more than 12° below normal, and that happens less than once in a year. There was also one very hot day above 40°, which is the normal number for January.
Rain fell, mainly as showers, on ten days spaced through the month. The higher readings were 28.4 mm on the 6th, 24.2 mm on the 23rd, and 27.6 mm on the 24th.

Weather log January 2016.

Comparing January months

Like January last year, this month could be called “moist”. It was cloudy, and the rather cool days were only 14° warmer than the nights.
The total rainfall of 104.7 mm was well above the average of 87 mm, and in the 72nd percentile. Again, there are no serious rainfall shortages for totals for any number of months. In fact, totals for 2-, 3-, 4-, 5-, and 6-months are very high.

El Niño

Manilla’s climate is now out of step with the El Niño – Southern Oscillation (ENSO). This “super” El Niño has not brought dryness here, and the dryness in January 2014 (for example) came at a time without an El Niño.

Climate for January 2016

Data. All data, including subsoil at 750 mm, are from 3 Monash Street, Manilla. Rainfall data up to 26/3/15 is from Manilla Post Office, Station 055031.

El Niño and My Climate

ENSO and Manilla NSW temperature anomalies over sixteen years


The first graph shows that the temperature at Manilla NSW agreed very closely with El Niño and La Niña temperatures for a good part of the last sixteen years.
The El Nino – Southern Oscillation (ENSO) is shown by NINO3.4 monthly anomaly values, and temperature at Manilla, NSW is smoothed monthly mean daily maximum temperature anomalies. (See the Note below.)
Values of Manilla temperatures agree with those of ENSO through the major temperature peaks and troughs in the spring seasons of 2002, 2006, 2007, 2009, and 2010. In the two highest peaks of 2002 and 2009 and the deep trough of 2010, Manilla temperature extremes were more than a month ahead of ENSO temperature extremes.
Since mid-2011, the two curves do not agree well:
* A La Nina in summer 2011-12 that was very weak produced the deepest of all troughs in Manilla temperature.
* An El Nino in winter 2012 resulted in heat at Manilla, but not until four months later.
* In spring 2013, when there was no El Nino at all, Manilla had a heat wave just like those with the El Nino’s of 2002 and 2009, .
The record for ENSO since January 2013 is unlike that earlier this century: it flutters rather than cycles.
To show slower changes, I have drawn cubic trend lines for both of the variables. These also agree closely, with ENSO going from a maximum (2004) to a minimum (2011) seven years later. Manilla temperature trends remained ahead of ENSO temperature trends by one or two years.


ENSO and Manilla NSW rainfall anomalies over sixteen years.

Continue reading

3-year trends to May 2015

Parametric plots of smoothed climate variables at Manilla
“May 2015: less moist”

Trends to May 2015

May raw anomaly data (orange)

May anomalies moved back towards normal from the cool moist conditions of April for the variables daily maximum temperature, rainfall, and daily temperature range. Cloudiness, however, became extreme, as it had been in the (smoothed data) a year earlier. Daily minimum temperature became very high, keeping the climate in the “Maritime” area. This, again, prevailed just one year ago.

Fully smoothed data (red)

The latest fully-smoothed data anomalies (November 2014) complete the season for spring 2014 (SON). As is most clearly seen in the graph at the bottom right, the daily maximum temperature anomaly (smoothed) reached a minor maximum just above normal in October. Other variables moved little. All were “normal” except the Dew Point anomaly, which had the low value of minus three that has now become normal.

Springtime temperature peaks

Each of the spring seasons of 2012, 2013 and 2014 marked a peak in daily maximum temperature anomalies. Spring 2013 was the hottest, breaking the record for this data set. Spring 2013 also had extreme peaks of lowest rainfall, least cloud, lowest dew point, and highest daily temperature range. This is just what is expected at an extreme El Niño, but there was no El Niño at that time.


Fully smoothed data – Gaussian smoothing with half-width 6 months – are plotted in red, partly smoothed data uncoloured, and raw data for the last data point in orange. January data points are marked by squares.
Blue diamonds and the dashed blue rectangle show the extreme values in the fully smoothed data record since September 1999.

Normal values are based on averages for the decade from March 1999.* They appear on these graphs as a turquoise (turquoise) circle at the origin (0,0). A range of anomalies called “normal” is shown by a dashed rectangle in aqua (aqua). For values in degrees, the assigned normal range is +/-0.7°; for cloudiness, +/-7%; for monthly rainfall, +/-14 mm.

 * Normal values for rainfall are based on averages for the 125 years beginning 1883.

Log of the Southern Oscillation Index with climate shifts

SOI plot with climate shifts

This graph relates to a graph of the cumulative values of the Southern Oscillation Index, posted earlier and copied below.

SOI CUSUM plotThe graph above is in a more familiar form . It may help to explain what the earlier graph means. That is, that the SOI was dominated by positive values (towards La Niña) for about fifty-nine years before 1976, and was dominated by negative values (towards El Niño) for twenty-four years after that date. From 2000 the trend seems to be upward, showing La Niña dominance again. Broadly, these were straight-line CUSUM  relationships throughout each of the periods, as shown by the coloured trend lines. Slopes on a CUSUM plot represent offsets of the mean monthly value: the mean SOI in the earlier period was +1.4 units, and that in the second period was -3.5 units. Since 2000, the mean monthly value is around +1.0 units. Continue reading

17 Years of “Droughts and Flooding Rains” at Manilla

Manilla 17-year smoothed rainfall anomaly record

Times when Dorothea Mackellar’s “droughts and flooding rains”* affected Manilla in the years from 1997 are shown by the wavy line on this graph. The climate swings in and out of times of high and low rainfall.

Peaks or troughs were often a year or two apart, but most of them were not very far from the normal rainfall value. Only two of the troughs were so far below normal that they were severe droughts: August 2002, and December 2013 (or maybe later). Milder droughts came in October 2006 and September 2009.
The rainfall in these 17 years was not below the long-term average, but slightly above it. As well as droughts there were two peaks of extremely high rainfall: in July 1998 (when the new Split-Rock reservoir suddenly filled) and in November 2011. These “deluges” had rainfall that was further from normal than the low rainfall in the droughts. Other rainfall peaks came in November 2005, October 2008, and October 2010.
In total, there were nine peaks and troughs with rainfall outside the normal range. Six of them came in the spring months of September, October or November.
Peaks and troughs in rainfall at Manilla quite often come near times of La Niña and El Niño. These are events in the record of Pacific Ocean temperatures called ENSO (El Niño – Southern Oscillation). The ENSO record for the last 17 years is shown in the second graph.

Continue reading

Predict weather from ENSO?

(Someone asked me to set down my thoughts about this.)

“Droughts and flooding rains” *

The climates of places in Australia cycle from hot, arid and dry, to cold, humid and wet every couple of years. (Dorothea Mackellar said she loved a sunburnt country “of droughts and flooding rains”*) This is a kind of quasi-biennial oscillation (QBO).  For more about the QBO, see this post, and the links in it. The cycles get weaker and stronger, more droughty or more rainy, and sometimes take about one year, sometimes three or more.

The climate of the Pacific Ocean has similar cycles, called the Southern Oscillation, discovered by Gilbert Walker a century ago. The pressure difference between Darwin and Tahiti oscillates in a way that reflects other widespread changes in climate. This is now called the El Niño – Southern Oscillation (ENSO) and it is monitored by sea-surface temperature east of Nauru in the Pacific Ocean, called NINO3.4. Now that we have up-to-date data on NINO3.4, the public has been led to believe that the data can be used to forecast Australian weather. It really can’t.

Problem No.1: Weather varies from place to place.

Every district in Australia has different weather, so one size does not fit all. Wasyl Drosdowsky made a map defining the regions that have consistent relationships to ENSO and other indices, but nobody has taken up the idea. (I would if I was boss of the Bureau of Meteorology!) Drosdowsky’s regions are rather similar to the States, but Victoria and the southern half of South Australia form a single region.

Problem No.2: Forecast is too late.

The ENSO cycle does not predict a cycle in any part of Australia because it happens at about the same time, and it takes a month or more to collate the data. Weather prediction from ENSO is always late. Consequently, there is a business to predict ENSO some months ahead. These predictions are very unreliable. Then the predictions of ENSO values are used to predict Australian weather, with vague statements of which regions will be affected.

To make matters worse, my Manilla data from 1999 shows that my weather happens in advance of the ENSO changes. I compared the ENSO log from 1999 to 2011 with smoothed daily maximum temperature anomaly, (1 month ahead)  smoothed monthly rainfall anomaly, (2 months ahead) and smoothed early morning dew point anomaly(3 months ahead). If droughts and deluges happen before peaks and troughs of ENSO at other places in Australia, this makes prediction from ENSO even less likely to work.

[Note added 14/07/2015. Updated graphs comparing the ENSO log from 1999 to 2014 with smoothed daily maximum temperature anomaly and smoothed monthly rainfall anomaly at Manilla are in this post. Manilla’s climate has not related very well to ENSO since mid-2011.]

[Note added 10/10/2019. Updated data confirm that ENSO lagged Manilla rainfall by 2 months from 1999 to August 2011, then failed to relate to Manilla rainfall after September 2011.
See: “21-C Rain-ENSO-IPO: Line graphs” and “21-C Rain ENSO IPO: Scatterplot”.
According to Power et al.(1999)Australian rainfall usually fails to relate to ENSO when the IPO goes positive, as it did from 2014 to 2017 (and 2018?).]

* By arrangement with the Licensor, The Dorothea Mackellar Estate, c/- Curtis Brown (Aust) Pty Ltd.

Data are cheap; information is expensive!

Originally posted on 12/5/2013 to a thread “ENSO Discussion 2013” on a “weatherzone forum.

Manilla Dew Point leads ENSO by three months

Manilla dew point lags NINO3.4 sea surface temperature by 3 months.

(This material justifies a statement in the post “Predict weather from ENSO?”)

The graphs above are like those in two previous posts, but show how Manilla smoothed monthly dew point anomalies, like temperature anomalies and rainfall anomalies, relate to the El Niño-Southern Oscillation (ENSO).

High (El Niño) values of Sea Surface Temperature (NINO3.4) are shown here to relate to low humidity at Manilla, NSW. As humidity data, I estimate dew points daily at sunrise. Dew points, like Sea Surface Temperatures, are expressed in degrees celsius, but corresponding anomalies take the opposite sense. The first graph plots the Manilla dew point anomaly, given a  negative sign, and the NINO3.4 anomaly. To improve the match, I have lagged the Manilla dew points by three months. As an example, I have noted on the graph the match of Manilla’s November 2005 humidity peak with the La Nina ENSO peak of February 2006.

To the eye, the over-all match is better than in either the rainfall or the maximum temperature plots of earlier posts. The two curves here match very well from 2000 to 2007.

The second graph shows the discrepancy between the two curves. Dashed lines show limits of a good match at +/-0.5 degrees. The nature of each larger discrepancy is noted. (“Here” in text boxes means “at Manilla”.)
After 2007 there are large mis-matches between Manilla dew point and ENSO. Dew point fluctuations suddenly become less than might be expected from NINO3.4 values. It may be relevant that, as I posted elsewhere  in July 2010, skies suddenly became very much cloudier at Manilla after August 2007.

I have also tried plotting the following variables against NINO3.4:

Daily minimum temperature;
Daily temperature range;
Percent cloudy mornings;
Subsoil temperature.

None of them matches NINO3.4 well enough to display.

The three sets of graphs show “teleconnections” between Sea Surface Temperatures in the equatorial Pacific and climate variables at Manilla in inland NSW, Australia. Climatic peaks come earlier at Manilla than in the Pacific:

Peaks of daily maximum temperature come one month earlier;
peaks of rainfall come two months earlier;
peaks of Dew Point come three months earlier.

In a simple-minded way, it seems to me more likely that Australia’s climate drives the Southern Oscillation than the other way around. I know that this is speculation. (Sort of like Abraham Ortellius suggesting in 1587 that Africa and South America might have drifted apart.)

1. High frequency noise is reduced in the case of the Manilla monthly data by a Gaussian smoothing function of half-width six months.
2. On advice, I represent the El Nino – Southern Oscillation phenomenon (ENSO) by the NINO3.4 area anomalies from the OISSTv2 data set.
My enquiries about the best data to use are in this “weatherzone”  thread.
The ensemble of sea surface temperatures does not have much high-frequency noise. There is some, however, and I have used the same smoothing as used in the (formerly authoritative) Oceanic Nino Index (ONI), that is, a running mean of each three monthly values.

This was posted originally in a “weatherzone” forum, with the date 12 November 2011. It is posted here with the nominal date 29 November 2011.