This post is the seventh 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 September
Extreme values of September anomalies in this period were all in the “Moisture Indicators” group:
Temperature range anomaly (minus) +3.6 deg: September 2010;
Cloudy days % anomaly +33%: September 2010;
Dew Point Anomalies (4) -3.8 deg: September 2011, -4.7 deg: September 2012, -4.9 deg: September 2013, -4.1 deg: September 2014.
Trend lines for September
The trend of daily maximum temperature anomalies was concave, with a minimum at 2007. The trend of mean temperature anomalies was similar, but less concave. The trend of minimum temperature anomaly was almost straight, but had a weak maximum in 2008 and ended low. The subsoil temperature anomaly trend was parallel to that of the daily maximum, but higher.
The Moisture Index trend line had a trough in 2002 followed by a peak in 2008. The rainfall trend line was more curved, with a trough in 2001 followed by a peak in 2009. The cloudy days anomaly trend began with the moisture index and ended with the rainfall. The trend of temperature range (minus) was simply convex, with a maximum at 2008, but also ended with the rainfall. As in August months, the dew point anomaly began high, paused near zero, and ended 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.