Category Archives: Australian Climate

The 2002 rainfall shortages at Manilla

Posted in 21st-century climate, Australian Climate, Climate events, Manilla NSW by

Graph of monthly percentile rainfall in a drought

In 2002, Manilla had a 6-month drought with one of the most extreme rainfall shortages on record. In nearly fifty years since 1966 there have been no other shortages like it.

I have discussed this drought in two posts: “Profile of an Extreme Drought”, and 3-year trends to August 2004 (An extreme 1-year drought).

[For an update on the longer and more extreme drought of 2018-19, see the note below.]

This post is about the rainfall record only. It compares the percentile values of rainfall totals for groups of months: one month, two months, and so on. The graph shows how the drought began, developed and faded. Other droughts may go through similar stages. I have plotted the pattern of rainfall shortages month by month, showing only even-numbered months. I have plotted them in different colours, with matching “Call-out” labels.

April 2002 (Red): no drought yet.
In April, the monthly rainfall was slightly below average: in the 40th percentile. In this month, nearly all rainfall totals up to the 42-month total were also below average. Only the 6-month total was above average. This set up the conditions for a drought. Notice that rainfall totals for periods longer than 42 months were all well above average. This hardly changed at all in this year. There had been a lot of rain in previous decades.

June 2002 (Orange): 2, 3, and 4-month droughts.
When May rainfall was in the 1st percentile and June rainfall in the 25th percentile, the June 2, 3, and 4-month totals became serious or severe shortages (below the 10th percentile).

August 2002 (Green): 2, 3, 4, 5, 6, and 9-month droughts.
With July rainfall again in the 1st percentile, and August rainfall in the 26th, the drought became extreme. The 4, 5, and 6-month totals were in the 1st percentile: few months had ever had such low figures.

October 2002 (Blue): 3, 4, 5, 6, 9, 12, 15, and 18-month droughts.
September and October both had rainfall in the 18th percentile. That relieved the short-term shortages somewhat, but not those in the medium term. Shortages in the 4, 5, and 9-month totals were in the 1st percentile, but the 6-month total was very much worse. At 76 mm, this 6-month total was the third driest on record, beaten only by August 1888 (43 mm) and September 1888 (69 mm).

December 2002 (Purple): only 9- and 12-month droughts remain.
November rainfall that was near average (40th percentile) and high December rainfall (84th percentile) broke the drought. Only some longer-term effects persisted as severe rainfall shortages in 9- and 12-month totals.

Rainfall status Jan-Feb 2019

Note added 2019.
Later such graphs in this blog have a logarithmic scale to distinguish the extreme rainfall shortages. Here is the one for the even more extreme drought of February 2019.

Hot Days and ENSO

Posted in 21st-century climate, Australian Climate, Climate Change, Manilla NSW, Weather extremes by

Graphical log of max temps and hot days

More frequent hot days do not come in a three year cycle, but in a 1.5 year cycle related to ENSO.

The Hot Day data set

The graph of number of hot days per year

Log of annual hot days in 16 yearsThe graph on the left is one I posted earlier. The height of each data point represents the number of hot days in a year, plotted near January. The pattern of points led me to join them by a smooth curve. This curve swings up and down rather regularly, with five peaks and five dips in the fifteen years. That is, more frequent hot days seem to come in a three-year cycle.
Is this cycle “real”? Should we look for a cause? Will the cycle continue?
Probably not! The points of measurement are one year apart. Cycles that are only three years long may be “aliases” of different and shorter undetectable cycles. (See Note below on Nyquist frequency.)

More detailed hot day data

Other graphs already shown include further data: the number of hot days in each month, and the 13-year average number of hot days in each calendar month. From these I have calculated a relative frequency. That is, the ratio of the actual number to the average number for that month.
Only the months of November, December, January and February have enough hot days to calculate a relative frequency, but these can show changes within the hotter months of each year.

The daily maximum temperature data set

A graph that I posted in “El Niño and my climate” shows a curve of smoothed monthly means of daily maximum temperature anomalies. The yearly cycle of summer-to winter temperature has been removed. I have also applied a smoothing function, which makes the monthly points of measurement effectively two or three months apart. As a result, cycles longer than about six months can be detected.
There are about 10 peaks and 10 dips in the 15.5 year curve. They define a cycle of about 1.5 years wavelength. That cycle is so much longer than the minimum-detectable six month cycle that “aliasing” is not likely.
The reality of this temperature curve is supported by its close similarity to the recognised curve of the El Niño – Southern Oscillation (ENSO), as read from NINO3.4 Pacific Ocean sea surface temperature anomalies.

A combined graph of hot day and temperature data

The graph at the top of the page presents the monthly smoothed maximum temperature anomaly again, using the scale at the left. To this I have added data on the number and frequency of hot days.
The annual number of hot days is shown in blue, in blue boxes. The boxes are placed higher or lower according to the number, but the height is adjusted to match other data better.
A “Hot Day Index” is shown by blue diamonds. This index is based on the relative frequency of hot days in each month that has data.  I have re-scaled the values to improve the match. (See Note on Re-scaling below.)

Matching hot days with temperature

Continue reading →

El Niño and My Climate

Posted in 21st-century climate, Australian Climate, Climate Change, Manilla NSW, World Climate by

ENSO and Manilla NSW temperature anomalies over sixteen years

Temperature

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.

Rainfall

ENSO and Manilla NSW rainfall anomalies over sixteen years.

Continue reading →

More Droughts After Heavier Rains III.

Posted in Australian Climate, Climate Change, Manilla NSW, Rainfall from 1883 by

Graphical log of errors when droughts are predicted from rains

Droughts and flooding rains at Manilla NSW were related in a way that is remarkable and unexpected.

Part III. Predicting drought from heavy rain

[Back to Part II: Scatter-plots]

The graph above is derived from the first graph in this series (copied here) by using the blue regression trend-line from the scatter plot of selected data (also copied here). (For data details, sLog of 1-year droughts and 5-year lagged heavy rainfallsee Note 1, below.)

The equation of the trend line, y = 0.030x is used AS IF to use the daily rainfall excesses to predict the drought frequency five years later. The graph shows the “error” of this “prediction”. (In Note 2, below, I concede that this data set could not support such prediction.)
As expected from the previous graphs, the “prediction” is accurate at most data points to 1975. It is correct to the nearest percentage whole number at nine of the eighteen points. From 1940 to 1955, droughts are uniformly more frequent than predicted. After 1975, the error curve swings wildly up and down.

Could droughts have been predicted from heavy rainfalls?

Scatter-plot 1890 to 1975

By about 1915, it is conceivable that this relationship could have been discovered, either by analysis of such data, or by modelling of the climate system. Then, the data for the next 20 years, up to 1935, would seem to confirm it. Data from 1940 to 1955 would cause doubts, but data from 1960 to 1975 would restore confidence. Then the utter failure of the model in the following four decades would have led to its abandonment, at least for the time being.

Climate shifts of 1975

Continue reading →

More Droughts After Heavier Rains II.

Posted in Australian Climate, Climate Change, Manilla NSW, Rainfall from 1883 by

Scatter-plot 1890 to 1975

Droughts and flooding rains at Manilla NSW were related in a way that is remarkable and unexpected.

Part II. Scatter-plots

[Back to Part I: Graphical logs]

I have made scatter plots to see how much correlation there is between the two data sets: the frequency % of severe 12-month drought and the total decadal daily rainfall excesses over 50 mm, when lagged five years. (For data details, see Note 1, below.)

A. The first 70% of the data

The first scatter-plot includes only the first 70% of the data, from 1890 to 1975, which showed matching patterns on the graphical log copied below. I have broken the data points into two groups: the aberrant group 1940 to 1955 (red) and the fourteen best-matched points (blue). The trend line that best fits those fourteen points is y = 0.028x + 0.407, with R-squared = 0.898. However, I have been able to fit the trend line y = 0.030x, that shows y proportional to x, without making R-squared worse than 0.892.
Similarly, the four decades centred on 1940, 1945, 1950 and 1955, had y = 0.050x, with R-squared equal to 0.902.

Expressed in words: for fourteen of the first eighteen data points, the frequency % of severe 12-month droughts remained close to 0.03 times the decade total of daily rainfall (>50 mm/day) measured five years earlier. For the other group of four adjacent points, the number was not 0.03, but 0.05.

B. All the data

Scatter-plot 1890 to 2010

The second scatter plot shows data for all 25 (five-year overlapped) decades. There is a “shot-gun” pattern, as expected. Continue reading →