October Climate Anomalies Log

Heat Indicators log for October months

This post is the eighth in a set for the 12 calendar months that began with March. Graphs are sixteen-year logs of the monthly mean anomaly values of nine climate variables for Manilla, NSW, with fitted trend lines. I have explained the method in notes at the foot of the page.

This series of posts gets more than its share of views. This is strange, as they contain little information. Comparing graphs for adjacent months shows widely different values and trends. In due course, I will compare all twelve months with each other. Perhaps that will yield interesting results, or perhaps not.

Raw anomaly values for October

Extreme values of October anomalies in this period were all in the “Moisture Indicators” group:

Cloudy days % anomalies (2) +31%: October 2010, 2011;
Dew Point Anomalies (5) +3.9°: October 1999, -3.9°: October 2002, -6.6°: October 2012, -7.8°: October 2013, -5.9°: October 2014.
Moisture Index (2) -3.1°: October 2012, -3.2°: October 2013.

Trend lines for October

Heat Indicators

The trend lines of daily maximum, mean and minimum temperature anomalies all had an early trough in 2001, a peak near 2006, and a trough near 2011. The daily minimum trend had the longer period and the larger amplitude. The subsoil temperature trend peaked early, in 2001, and had a very broad trough around 2009.

Moisture Indicators log for October months

Moisture indicators

The Moisture Index trend line had a trough in 2002 followed by a peak in 2008; trends for rainfall and for (minus) temperature range were similar.
The cloudy days anomaly trend had a much higher peak, delayed to 2011. The dew point anomaly began high, was low in 2002, high in 2007, and ended very low.


Notes.

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 anomaly values vary a lot from month to month, and different variables often do not move in the same sense.
(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

Dew point values are problematic. Early values were estimated from remote readings, some of which seem too high; recent values use an instrument capable of reading very low humidity values, some of which are surely too low.

Trend lines

Changes in raw anomaly values are very large from year to year and show no clear pattern. To reveal a pattern calls for trend lines to be fitted.
When I fit linear trend lines, they have almost no meaning. They have R-squared values around 0.01! That is, linear trend lines “explain” hardly any of the variation. When I fit trend lines that are parabolic, cubic, or quartic the R-squared value goes up, until it is around 0.3 for quartic trends. (Quartic trends “explain” about 30% of the variation.) Beyond quartic functions, there are not enough data points to justify fitting the trend line.
Quartic trend lines can identify up to three local extreme points, whether maxima or minima, if they exist in the data.

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