When is the First Frost?

This year (2017) the first frost at Manilla came on the 11th of May, close to the middle date for it: the 13th of May. In just half of the years, the first frost comes between ANZAC Day (the 25th of April) and the 19th of May.

Graphical record of first frost dates
(See the notes below: “Observing Frosts in Manilla.”)

The date of first frost from year to year

The graph shows the dates of first frosts in the last nineteen years. One feature stands out: from a very early date of the 4th of April in 2008, the dates got later each year to a very late date of the 6th of June in 2014. Otherwise, the dates simply jumped around.

Graphical log of frostsThe date of first frost hardly relates at all to the number of frosts in a season. This graph, copied from an earlier post, shows the mismatches. The earliest first frost, in 2008, was in a year with a normal number of frosts. In the least frosty year, 2013, the first frost did not come late.

The central date and the spread

To find the central value and the spread of a climate item like this calls for readings for a number of years called a “Normal Period”. (See note below on Climate Normals.) I chose the first eleven years of my readings (1999 to 2009) as my Normal Period. For this period I found these five order statistics:

Lowest (earliest) value: 4th April;
First Quartile value: 25th April (ANZAC Day);
Median (middle) value: 13th May;
Third Quartile value: 19th May;
Highest (latest) value: 24th May.

These five values divide the dates of first frost into four equal groups. For example, the first frost comes before ANZAC Day in one year out of four. This could confirm what Manilla gardeners know already!

Is the first frost getting later?

Talk of global warming leads us to expect the date of first frost to get later. By how much?
Dates on the graph after 2009 seem to be later in the season than during the Normal Period. As shown, a linear trend line fitted to the data points slopes steeply down towards later dates in later years. A curved trend line (a parabola) slopes down even more steeply. However, with so few data points, these trend lines are wild guesses, not to be relied on for forecasting future frosts.
Data for NSW from 1910 shows that daily minimum temperatures have been rising at 0.11° per decade.  (That is much faster than the rate for daily maximum temperatures, which is 0.07° per decade.) To work out how this might affect the date of first frost in Manilla, one needs to know that the daily minimum temperature in this season gets lower each day by 0.15°. One day of seasonal cooling will more than cover a decade of climate warming. The effect of global warming is to make the date of first frost only one day later in fourteen years. If the middle date of first frost was the 13th of May in the Normal Period, centred on 2004, the forecast middle date of first frost next year (2018) would be the 14th of May. This is shown by the flattest of the three trend lines on the graph.

Looking ahead, it seems unlikely that the date of first frost will get later by as much as a week within a lifetime.


Notes

1. Observing Frost in Manilla

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Seasons were strange in 2016

In the year 2016, the seasonal climate cycles at Manilla, NSW were abnormal. Heat and cold, moisture and dryness did not come at the usual times.

Temperature and rainfall graphs

Mean monthly temperature

 

Graphs of monthly temperatures, normal and 2016The first graph shows the mean temperatures for each calendar month, both in a normal year (red) and in the year 2016 (blue). In 2016. earlier months, such as April, were warmer, and later months, such as October, were cooler. The difference (anomaly) is plotted below. Anomaly values in this year rise and fall rather steadily in a single cycle that lags months behind the normal summer-winter cycle. The amplitude of this anomaly cycle in 2016 is 5.3 degrees, which is nearly one third of the normal summer to winter amplitude of 16.4 degrees.

 

Monthly total rainfall

Graphs of monthly rainfall totals, normal and 2016In the same format, the second graph shows the rainfall totals for each calendar month, both in a normal year (red) and in the year 2016 (blue). The mid-year months of June, August, and September, usually dry, were very wet in 2016. The anomaly graph adds to this that rainfall was very low in February, March and April, and again in November and December. Rainfall anomaly does not show such a clear cycle as temperature does, but the effect is bigger. The difference in anomaly between September (+80 mm) and November (-40 mm) is 120 mm, while normally the difference between the wettest month (January) and the driest month (April) is only 48 mm.

Climate anomaly graphs and trends for 2016

The other two graphs add more climate anomaly variables and show the trends through the year 2016.
[See Notes below for an explanation.]

Monthly heat anomalies for 2016

Heat anomalies and trends

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Climate trends for thermal soaring

For pilots who soar at Lake Keepit or Mount Borah: relevant summer climate data for Manilla, NSW, since 1999.

Graph of some summer climate variables 1999 to 2015.

Variables relevant to thermal soaring

From my data I have selected three variables that are relevant to success in soaring flight using thermals. I have chosen to use values for summer: a total or average for the three months of December, January and February.
The variables are:

  • The number of hot days, when the maximum temperature was over 33°C;
  • The number of sunny days, when the cloud amount seen at 9 am was less than two octas;
  • The average daily temperature range in degrees celsius.

Changing values of the variables

The graph shows that each variable fluctuated wildly, with each summer very different from the last. These variables often moved in the same sense.
Two summers had high values of all three variables: 2006-07 and 2013-14. Two summers had low values of all three variables: 2007-08 and 2011-12. I would expect that longer and faster thermal soaring flights would have been achieved in the summers with high values, compared to those with low values.

Trends

I have fitted linear trend lines, and displayed their equations within the graph.
All three trend lines slope down. This suggests that summer thermal soaring conditions have been getting worse.
I have cited the values of “R-squared”, the Coefficient of Determination. All three R-squared values are abysmally low. Even the best is below 20%, which can be taken to mean that more than 80% of the variation has nothing to do with the trend line shown.
You could say that the trends are nonsense, but we are dealing with Climate Change here!

The future

In the spirit of Mark Twain, we can extend the trend lines forward to where they come to zero:

  • There will be no hot days above 33° by the summer of 2118;
  • There will be no sunny mornings with less than 2 octas of cloud by 2073;
  • Days will be no warmer than nights by 2423.

That last date seems too remote to worry about. However, the daily temperature range will be unacceptable when it gets down to 11°. That is the current summer value for Lasham, England, after all. According to the trend, the daily temperature range will be worse than at Lasham by 2117. That is the same year that the very last 33° day is expected.

Global Warming

You may be surprised that the linear trend lines fitted to this data set slope downwards. It seems to contradict Global Warming. Continue reading

Record warm nights

Two very warm nights

On two mornings this month, the 21st and 23rd, the minimum temperature in my thermometer screen was higher than it has ever been in July. That is, in the 17-year record that I began in March 1999.
These minimum readings were 14.3° and 14.4°. The highest July minimum had been 13.5° (31/07/2010), and only five readings had ever been above 12°. Such readings are more than ten degrees higher than normal in July.
In fact, one of the nights was much warmer than the minimum temperature indicates.

Minima not always at night

High minimum temperature readings are the usual evidence of warm nights. Unfortunately, they are not the same thing. Especially in the case of very high readings, they can be misleading.
The catch is that we expect daily maximum temperature to occur in daytime, when the sun is in the sky, and daily minimum temperature to occur at night, when it is not. In Manilla’s very sunny climate, the maximum is usually about fifteen degrees higher than the minimum. We can expect the maximum about 3 pm, and the minimum about 6 am.
When a thermometer is read at 9 am (as they are), the maximum reading recorded on it is usually that of the afternoon, and the minimum reading that of just before sunrise. Usually, but not always!
Times when nights are very warm are usually cloudy. The clouds form a blanket that keeps us warm. Because they also block the sun in daytime, the daily maximum temperature may be almost the same as the daily minimum. The times when maxima and minima occur may become vague. As a complication, warm nights tend to happen when warm air comes to us on the wind. Then much colder air often follows. If the cold air arrives before 9 am, it will lower the minimum temperature reading, destroying the evidence of a warm night.

Thermograph Traces

I do not have a thermograph that makes a continuous temperature trace. The trace for Tamworth Airport can be seen here. (Choose a date.)

For this month’s warmest night, the Tamworth thermograph trace shows that the daily minimum temperature value is misleading due to these factors. The night was much warmer.
Between 5:30 pm on the 22nd and 5:30 am on the 23rd, the lowest temperature, which came at 7:40 pm, was 17.8°. Most of the night, from midnight to 4:00 am, was above 20°! Yet the (Tamworth) minimum for the 24 hour period was 12.5°.
Earlier in the month, in the 24 hours to 9 am on the 6th, the conventional maximum and minimum values were highly misleading. The daily maximum was the very last reading (10.0°) and the daily minimum the very first, 24 hours earlier (6.5°). The afternoon maximum temperature was 8.0° and the pre-dawn minimum temperature was 8.2°. On that date, the day was 0.2° cooler than the night!

Ranked Hot and Cold Days

Graphs like this show how the trends of temperature differ between the coldest days (or nights) of the year, the hottest ones, and all those ranked in between.

This first post on this topic is a “sampler” of Manilla data that I will present. It compares my first 9-year period March 1999 to February 2008 with the 9-year period September 2003 to August 2012, four and a half years later.

Graphs showing trends of temperatures for ranked days.

All the days (or nights) of the year are arranged from the coldest on the left to the hottest on the right. Columns show by how much the day or night of that rank has trended warmer or cooler during the nine years. (See also Notes below.)

1. Days
In the earlier period (blue), most winter days and a few mid-summer days cooled at 0.1 to 0.2 degrees per year. Days in spring and autumn, and cooler days in summer warmed at less than 0.1 degrees per year.
In the later period (red), all days of the year cooled, but there was a gradient from no cooling in midwinter to extremely rapid cooling (more than 0.3 degrees per year) in midsummer.

2. Nights
In the earlier period (blue), nights in the warmer half of the year, and in midwinter warmed at about 0.1 degrees per year. There was no warming either in midsummer or in the warmer part of winter.
In the later period (red), it was now in the cooler half of the year that nights warmed at about 0.1 degrees per year. Nights in the warmer part of summer cooled more and more rapidly as they approached midsummer, where the cooling rate was 0.25 degrees per year.
[The 50-year average warming of this part of australia is 0.015 degrees per year. That is, less than two tick-marks on the y-axis.]


Prior postings

This graph and its commentary appeared as a post in “weatherzone” forums on 25/10/12:

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The 2002 rainfall shortages at Manilla

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).

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, it 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.

Hot Days and ENSO

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

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