This post is the fourth in a set for the 12 calendar months. 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 June
Extreme values of June anomalies in this period were:
Daily Maximum Temperature anomaly -3.5 deg: June 2007;
Daily Minimum Temperature anomaly +3.1 deg: June 2009;
Subsoil Temperature anomaly +3.2 deg: June 2013;
Rainfall Anomaly +65 mm: June 2005
Temperature range anomaly (minus) +4.1 deg: June 2007;
Temperature range anomaly (minus) +3.5 deg: June 2013;
Percent Cloudy Days +40%: June 2013.
Trend lines for June
The trend of mean temperature rose from zero at first to stay at 0.5 deg from 2004 to 2010, then rose again.
The trends of daily maximum and daily minimum temperature anomalies were mirror-reversed about the mean trend line. The maximum line reached a peak in 2003 and a trough in 2009, while the minimum line did the reverse. The subsoil temperature anomaly trend was slightly low in 2004 and was high in 2014.
Moisture indicator trend lines for June generally rose during this period. Cloudy days increased strongly and steadily. Other variables had a strong minimum about 2001-2, a maximum about 2007-9 and a weak minimum about 2012.
Each data point is an anomaly value that is the difference beween the mean value for a month and the normal value for that calendar month based on the decade beginning March 1999.
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 rainfall anomalies 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.