3-year trends to May 2015

Parametric plots of smoothed climate variables at Manilla
“May 2015: less moist”

Trends to May 2015

May raw anomaly data (orange)

May anomalies moved back towards normal from the cool moist conditions of April for the variables daily maximum temperature, rainfall, and daily temperature range. Cloudiness, however, became extreme, as it had been in the (smoothed data) a year earlier. Daily minimum temperature became very high, keeping the climate in the “Maritime” area. This, again, prevailed just one year ago.

Fully smoothed data (red)

The latest fully-smoothed data anomalies (November 2014) complete the season for spring 2014 (SON). As is most clearly seen in the graph at the bottom right, the daily maximum temperature anomaly (smoothed) reached a minor maximum just above normal in October. Other variables moved little. All were “normal” except the Dew Point anomaly, which had the low value of minus three that has now become normal.

Springtime temperature peaks

Each of the spring seasons of 2012, 2013 and 2014 marked a peak in daily maximum temperature anomalies. Spring 2013 was the hottest, breaking the record for this data set. Spring 2013 also had extreme peaks of lowest rainfall, least cloud, lowest dew point, and highest daily temperature range. This is just what is expected at an extreme El Niño, but there was no El Niño at that time.


Note:

Fully smoothed data – Gaussian smoothing with half-width 6 months – are plotted in red, partly smoothed data uncoloured, and raw data for the last data point in orange. January data points are marked by squares.
Blue diamonds and the dashed blue rectangle show the extreme values in the fully smoothed data record since September 1999.

Normal values are based on averages for the decade from March 1999.* They appear on these graphs as a turquoise (turquoise) circle at the origin (0,0). A range of anomalies called “normal” is shown by a dashed rectangle in aqua (aqua). For values in degrees, the assigned normal range is +/-0.7°; for cloudiness, +/-7%; for monthly rainfall, +/-14 mm.

 * Normal values for rainfall are based on averages for the 125 years beginning 1883.

More Droughts After Heavier Rains III.

Graphical log of errors when droughts are predicted from rains

Droughts and flooding rains at Manilla NSW were related in a way that is remarkable and unexpected.

Part III. Predicting drought from heavy rain

[Back to Part II: Scatter-plots]

The graph above is derived from the first graph in this series (copied here) by using the blue regression trend-line from the scatter plot of selected data (also copied here). (For data details, sLog of 1-year droughts and 5-year lagged heavy rainfallsee Note 1, below.)

The equation of the trend line, y = 0.030x is used AS IF to use the daily rainfall excesses to predict the drought frequency five years later. The graph shows the “error” of this “prediction”. (In Note 2, below, I concede that this data set could not support such prediction.)
As expected from the previous graphs, the “prediction” is accurate at most data points to 1975. It is correct to the nearest percentage whole number at nine of the eighteen points. From 1940 to 1955, droughts are uniformly more frequent than predicted. After 1975, the error curve swings wildly up and down.

Could droughts have been predicted from heavy rainfalls?

Scatter-plot 1890 to 1975

By about 1915, it is conceivable that this relationship could have been discovered, either by analysis of such data, or by modelling of the climate system. Then, the data for the next 20 years, up to 1935, would seem to confirm it. Data from 1940 to 1955 would cause doubts, but data from 1960 to 1975 would restore confidence. Then the utter failure of the model in the following four decades would have led to its abandonment, at least for the time being.

Climate shifts of 1975

Continue reading

More Droughts After Heavier Rains II.

Scatter-plot 1890 to 1975

Droughts and flooding rains at Manilla NSW were related in a way that is remarkable and unexpected.

Part II. Scatter-plots

[Back to Part I: Graphical logs]

I have made scatter plots to see how much correlation there is between the two data sets: the frequency % of severe 12-month drought and the total decadal daily rainfall excesses over 50 mm, when lagged five years. (For data details, see Note 1, below.)

A. The first 70% of the data

The first scatter-plot includes only the first 70% of the data, from 1890 to 1975, which showed matching patterns on the graphical log copied below. I have broken the data points into two groups: the aberrant group 1940 to 1955 (red) and the fourteen best-matched points (blue). The trend line that best fits those fourteen points is y = 0.028x + 0.407, with R-squared = 0.898. However, I have been able to fit the trend line y = 0.030x, that shows y proportional to x, without making R-squared worse than 0.892.
Similarly, the four decades centred on 1940, 1945, 1950 and 1955, had y = 0.050x, with R-squared equal to 0.902.

Expressed in words: for fourteen of the first eighteen data points, the frequency % of severe 12-month droughts remained close to 0.03 times the decade total of daily rainfall (>50 mm/day) measured five years earlier. For the other group of four adjacent points, the number was not 0.03, but 0.05.

B. All the data

Scatter-plot 1890 to 2010

The second scatter plot shows data for all 25 (five-year overlapped) decades. There is a “shot-gun” pattern, as expected. Continue reading

More Droughts After Heavier Rains I.

Log of 1-year droughts and 5-year lagged heavy rainfalls

Droughts and flooding rains at Manilla NSW were related in a way that is remarkable and unexpected.

Part 1. Graphical logs

As the first graph shows, for most of the 130-year record year-long droughts came in direct proportion to very heavy daily rainfall five years earlier. (For data details, see Note 1, below.)
The match between these two variables is astonishing. Both are based on rainfall readings, but they are scarcely related. Excessive daily rainfalls are transient extreme weather events; 12-month droughts are an aspect of climate.

Mackellar’s “Droughts and flooding rains”

Dorothea Mackellar’s famous line * is more apt for this graph than for other graphs where I use “flooding rains” to mean periods unlike drought. (See Note 2. below.) The rains and droughts that I plot here both bring hardship. Severe droughts lasting one year are among the worst of droughts: long enough to use up reserves, and not so long as to be eased by periods of rain. The daily rainfall events plotted are the ones that cause damaging floods.

Features of the graphical log

Log of 1-year droughts and heavy rainfalls

This second graph shows the data at the actual dates. Although the data points for the decade excess of heavy daily rainfall and those for frequency % of 12-month droughts have a matching pattern for much of the record, the pattern is offset. Heavy rainfall points come five years earlier than corresponding drought points. Notice that the heavy rainfalls do not (except in 1980) come squarely in gaps between droughts.
Lagging the rainfall points by five years (as in the first graph) makes some matches almost exact. Such matches occur at all data points from 1890 to 1975, except those from 1940 to 1955, where drought frequencies are relatively higher. Both variables show a two-decade-long, slow decline from 1905 to 1925. At the chosen scales, the amplitude of corresponding rises and falls are usually similar as well.
After 1975, daily rainfall oscillates through a wide amplitude with a twenty-year period, while the frequency % of drought varies Continue reading

3-year trends to April 2015

Parametric plots of smoothed climate variables at Manilla
“April 2015: equable”

Trends to April 2015

  April raw anomaly data (orange)

In April, daily maximum temperature anomaly became very low (-2.1°) while daily minimum temperature anomaly remained high (+0.7°). Other anomalies, except subsoil temperature, moved down the graphs, showing moist conditions. The extremely low temperature range anomaly (-3.0°) shows that the climate was equable, as it had been in the spring of 2010 (a smoothed record value).

Fully smoothed data (red)

The latest fully-smoothed data anomalies (October 2014) moved little, being warm and slightly dry.

Loops in the subsoil anomaly graph

The parametric plot of subsoil temperature anomaly against that of daily maximum temperature (bottom right) shows several clockwise loops. That is, peaks or troughs of subsoil temperature precede those of daily maximum (air) temperature by a month or more. This is not what one would expect. Indeed, where graphs of these variables earlier in this sixteen-year record show such loops, they are always anti-clockwise. Subsoil temperature anmalies lag those of daily maximum air temperature. See the graphs for August 2002, August 2004, August 2006, August 2008, May 2010, and April 2012.

In the last mentioned graph, the three extreme points included show no lag between the two variables. That period, from early 2009 to late 2011 marks the transition from a stable regime of subsoil temperature lagging daily maximum air temperature to the current regime of subsoil temperature leading daily maximum air temperature.


Note:

Fully smoothed data – Gaussian smoothing with half-width 6 months – are plotted in red, partly smoothed data uncoloured, and raw data for the last data point in orange. January data points are marked by squares.
Blue diamonds and the dashed blue rectangle show the extreme values in the fully smoothed data record since September 1999.

Normal values are based on averages for the decade from March 1999.* They appear on these graphs as a turquoise (turquoise) circle at the origin (0,0). A range of anomalies called “normal” is shown by a dashed rectangle in aqua (aqua). For values in degrees, the assigned normal range is +/-0.7°; for cloudiness, +/-7%; for monthly rainfall, +/-14 mm.

 * Normal values for rainfall are based on averages for the 125 years beginning 1883.