This post is the second 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 April
April anomalies had few extreme values in this period:
Daily Maximum Temperature anomaly +3.4°: April 2005;
Morning Dew Point Anomaly -3.3°: April 2008;
Morning Dew Point Anomaly -4.8°: April 2013;
Rainfall anomaly +79 mm: April 2003.
Trend lines for April
The trends of all three air temperature anomalies were almost the same. They began very low in 1999, reached a maximum in 2002-3 and a minimum in 2009-10. The trend of subsoil temperature began similarly, but with much less variation, and peaked in 2012.
Trends of most moisture indicators, including rainfall, began a little high and reached a very shallow minimum around 2004 to 2008. The trend of percent cloudy days reached a peak of nearly +10% in 2013, while that of dew point began rather high and finished rather low.
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.