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

The 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

In 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.]

Heat anomalies and trends

The third graph shows anomalies of not only the monthly mean air temperature, but also the monthly mean maximum and mean minimum air temperatures, and the monthly mean subsoil temperature (at 750 mm).
In this year, the trend curves for all the temperature anomalies were similar, with a peak in autumn and a trough in spring.They differed, however. Maximum temperature (in red) had the earliest peak (March) and trough (September). Mean temperature (in black) came a month later, and minimum temperature (in blue) a month later still. Subsoil temperature (in green) kept pace with minimum (air) temperature, but was even later to hit a trough (November).
These smooth trend curves show how, in this year, changes in daily maximum temperature ran about two months earlier than those of both daily minimum temperature and subsoil temperature. It suggests to me that the daily maximum temperature is the more direct response to imposed climate, while the other two follow.
The raw anomaly data for October, November, and December explain how the subsoil temperature anomaly for December was so low when the air temperature anomalies were so high. Anomaly values for all had been very low in October. Those for the air temperatures then rose extremely rapidly: 5 degrees in two months. I do not think the subsoil can be warmed up so rapidly. (The actual mean monthly air temperature rose by 10.0 degrees from October to December, while the subsoil temperature rose by 6.2 degrees.)

Moisture anomalies and trends

The fourth graph shows anomalies and trend lines for a number of variables expressing climatic moisture.
The trend lines are remarkably similar, peaking in mid-year and with troughs early and late. Dew point peaks as early as June, and rainfall peaks as late as August.

Context

One feature of the pattern of anomalies this year is normal, as shown over and over in data from this site: rainfall relates inversely to temperature. Through the eight months from February to September, as rainfall anomaly increased, daily maximum temperature anomaly decreased. Data in the 3-year trend graph agree, and suggest that a major cool-wet peak passed in September 2016. However, the latest very warm-dry (“drought”) peak had been years earlier: late in 2013.

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Notes on monthly anomaly graphs.

Graphs like this have been posted in the category “Calendar month anomalies”.

In that series, each post was for one named calendar month. Graphs showed how the anomalies for that month varied through sixteen years of record.
Each post ended with notes to explain the graphs, like the following:

Anomaly values

Each data point is an anomaly value that is the difference between the mean value for a month and the normal value for that calendar month. Normals are based on the decade beginning March 1999, except that rainfall normals are based on 125 years from 1883.
(Raw values for variables in a given month are in a report for that month. Look for the report for a given month in the “Archive” for the month following it.)

Heat Indicators

Four of the anomalies of variables are grouped as indicators of the anomaly of sensible heat at the site: daily maximum air temperature, daily minimum air temperature, daily mean air temperature (mean of maximum and minimum) and subsoil temperature (at 750 mm).

Moisture Indicators

The anomalies of five more variables are grouped as moisture indicators relating to latent heat rather than sensible heat. They are: rainfall total (mm), percent cloudy mornings (>4 octas), early morning dew point, daily temperature range (minus), and a composite measure called “Moisture Index”. For plotting, the observed anomaly values of percent cloudy mornings have been divided by ten and the observed anomalies of monthly total rainfall in millimetres have been divided by twenty. In the same way, the moisture index is calculated as: MI = ((Rf anom/20)+(%Cloudy anom/10)+(DP anom)+(-(TempRange anom)))/4

Trend lines

Changes in raw anomaly values are large from month to month. To reveal a pattern calls for trend lines to be fitted.
Quartic trend lines can identify up to three local extreme points, whether maxima or minima, if they exist in the data. Beyond quartic functions, there are not enough data points to justify fitting the trend line.