This post is the eleventh 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 January
Extreme values of January anomalies were as follows:
Daily Maximum Temperature Anomalies (1) -3.7 deg: January 2012;
Rainfall Anomalies (5) -70 mm: January 2002; -75 mm: January 2003; +80 mm: January 2004; +94 mm: January 2006; -85 mm: January 2014;
Dew Point Anomalies (2) +3.1 deg: January 2006; -7.4 deg: January 2014.
Trend lines for January
All heat indicator quartic trends began low and ended slightly high, and had a low peak in 2003, -05, or -06, and a shallow trough about 2012.
The quartic trend line for rainfall was like that for minimum temperature, while that for cloudy days was the inverse(!). The trend for Minus (Temperature Range Anomaly) began low, then became flat. The trend for dew point was strongly convex, peaking in 2007, and that for the generalised Moisture Index was less convex.
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
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.
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.