This post is the fifth 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 July
Extreme values of July anomalies in this period were:
Subsoil Temperature anomaly +3.2 deg: July 2012;
Temperature range anomaly (minus) -3.2 deg: July 2002;
Dew Point Anomaly +3.3 deg: July 1999;
Dew Point Anomaly -3.8 deg: July 2002.
Trend lines for July
The trend of mean temperature anomalies fell below zero in 2003 then rose to +1 by 2014.
The trend of daily maximum temperature anomalies fell more slowly to -0.5 in 2007, then rose to meet the mean in 2014. The trend of minimum temperature anomaly reached an early minimum in 2002. It rose to a broad peak (+1.4) in 2010, then declined. The subsoil temperature anomaly trend was slightly low in 2004 and very high (+1.9) in 2013.
Moisture indicator trend lines generally had troughs followed by peaks. Dew Point anomaly began very high, fell to -1.0 in 2003, rose to zero in 2008, and ended very low. Temperature Range Anomaly (minus) rose to a very high (+1.8) peak in 2009. Percent Cloudy Days Anomaly reached a lower peak in 2011. Rainfall Anomaly peaked twice, in 2001 and in 2012, with a trough in 2006.
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.