This post is the ninth 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.
Raw anomaly values for November
Extreme values of November anomalies were as follows:
Daily Maximum Temperature Anomalies (4) +3.6 deg: November 2002; +5.5 deg: November 2009; +3.0 deg: November 2012; +5.0 deg: November 2014;
Daily Mean Temperature Anomalies (2) +4.6 deg: November 2009; +4.0 deg: November 2014;
Daily Minimum Temperature Anomalies (1) +3.8 deg: November 2009;
Rainfall Anomalies (4) +65 mm: November 2000; +66 mm: November 2001; +65 mm: November 2008: +176 mm!: November 2011;
Dew Point Anomalies (2) -5.4 deg: November 2013; -4.1 deg: November 2014;
Moisture Index (1) +3.3 deg: November 2011.
Trend lines for November
All heat indicator quartic trends began low and ended high. The trends for daily maximum and for subsoil had a peak in 2003 or 2004 and a trough in 2008 or 2010. The trend for daily mean was constant from 2004 to 2008, while the trend for daily minimum persistently rose, at a reducing rate.
Quartic trend lines for all moisture indicators began high, had a trough about 2002 and a peak about 2010, and ended low. The beginning and the peak were highest for rainfall. The end point for dew point was very low.
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.)
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).
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
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.