Category Archives: Anomalies smoothed

Logs of all monthly values of particular climate variables are smoothed, generally with a six month wide Gaussian function.

Hot and dry records in January 2019

Posted in 3-year climate trends, Anomalies smoothed, Manilla NSW by

In January 2019, the smoothed anomaly value of monthly rainfall reached a record low (-31.8 mm/month), and that of monthly mean maximum temperature a record high (+1.79°).

Rainfall and temperature trajectory Sep2016 toFeb 2019

[This graph is extracted from a forthcoming post in the series “3-year trends…”. ]

This graph shows temperature and rainfall anomalies, not raw data. It shows how far the actual values differ from normal. The 30 data points from September 2016 to February 2019 (coloured red) are smoothed to show only cycles longer than one year.

The earliest data point, September 2016, had temperature and rainfall just beyond the normal range on the cool and wet side (lower left). Since that date, all the smoothed data points have fallen close to the sloping Mackellar trend line (blue) from cool-wet to hot-dry. (See the note below: Mackellar trend line.)
In the 4 months up to January 2017, warming and drying was rapid, passing completely through the “normal” range. Next, some cooling and wetting occurred to May 2017, then warming and drying resumed to a full drought in March 2018. Through the year 2018, drought prevailed, with a rainfall anomaly always below -25 mm. The temperature anomaly fell to only +1 deg by August, but rose again while rainfall fell. Records for low rainfall and high temperature were broken repeatedly.
January 2019 had the lowest smoothed monthly rainfall anomaly of the 21st century (-31.8 mm/month), and the highest monthly mean maximum temperature (+1.79°).
The following month, February 2019, had a slightly decreased temperature anomaly, and an increased rainfall anomaly. Later data points with less smoothing applied suggest that the smoothed record values of January may stand for some time.

Note.

Mackellar trend line

The insight of Dorothea Mackellar that this is a land “of droughts and flooding rains” *
is expressed in these graphs by a blue trend line passing through the “Normal” point in the centre (aqua) and extending both to “Droughts” with high temperature and low rainfall at the top right and to “Flooding Rains” with low temperature and high rainfall at the bottom left. Smoothed data points for anomalies of mean monthly daily maximum temperature and monthly rainfall totals generally lie close to the sloping blue line in such graphs for all of the last 20 years. (Search “3-year climate trends”, this one, for example).

Notice that record high and low values of smoothed anomalies of rainfall and daily maximum temperature (dates marked in blue) lie close the this blue line, supporting the estimate.
Empirically, one degree of increase in temperature anomaly matches 20 mm of decrease in monthly rainfall anomaly: the Mackellar Constant for Manilla is -20mm/month/degree.

* By arrangement with the Licensor, The Dorothea Mackellar Estate, c/- Curtis Brown (Aust) Pty Ltd.

21-C Climate: Mackellar cycles

Posted in 21st-century climate, Anomalies smoothed, Australian Climate, Climate Change, Manilla NSW by

At Manilla, NSW, the anomaly of daily maximum temperature has continued to track, in the opposite sense, that of monthly rainfall.

Graphical log of smoothed rainfall and temperature.

The values shown are anomalies from normal values, smoothed to suppress cycles shorter than 12 months. (See notes below on Normals and Smoothing.)

The pattern is of quasi-biennial cycles that express the insight of Dorothea Mackellar that this is a land “of droughts and flooding rains“*. Hot dry times alternate with cool wet times. For temperature, I have chosen the mean daily maximum, as it best matches the rainfall.

This post updates others in the Menu Category “Manilla NSW/21st century climate/Anomalies smoothed”, such as “17 years of ‘Droughts and Flooding Rains’ at Manilla” (29/06/2014).

“Droughts” (hot dry times)

Winter-spring 2002. The drought of 2002 was extreme, having rainfall in the lowest 1% in history. Lowest rainfall anomaly was in the winter and highest temperature anomaly in the spring.

Spring 2009. The temperature anomaly in spring 2009 was as high as in 2002, but the rainfall (as smoothed) barely qualified as “drought”.

Spring-summer 2013. The maximum temperature anomaly in spring 2013 was again like that in 2002 and 2009. This time, the rainfall minimum came later, in the summer. The drought was severe but not extreme.

Autumn-winter 2018. The temperature anomaly peak was higher than the earlier peaks. The minimum rainfall anomaly that followed in the winter was again extreme.

Summer 2018-19. At this time, the temperature anomaly was the highest, and the rainfall anomaly the lowest on this graph.

“Flooding Rains” (cool wet times)

Spring 2005. The spring of 2005 was wet, but the temperature was not cool but rather warm.

Summer 2007-8. Although the summer of 2007-8 was cool, rainfall was normal. Continue reading →

17 Years of “Droughts and Flooding Rains” at Manilla

Posted in 21st-century climate, Anomalies smoothed, Australian Climate, Climate Change, Manilla NSW by

Manilla 17-year smoothed rainfall anomaly record

Times when Dorothea Mackellar’s “droughts and flooding rains”* affected Manilla in the years from 1997 are shown by the wavy line on this graph. The climate swings in and out of times of high and low rainfall.

Peaks or troughs were often a year or two apart, but most of them were not very far from the normal rainfall value. Only two of the troughs were so far below normal that they were severe droughts: August 2002, and December 2013 (or maybe later). Milder droughts came in October 2006 and September 2009.
The rainfall in these 17 years was not below the long-term average, but slightly above it. As well as droughts there were two peaks of extremely high rainfall: in July 1998 (when the new Split-Rock reservoir suddenly filled) and in November 2011. These “deluges” had rainfall that was further from normal than the low rainfall in the droughts. Other rainfall peaks came in November 2005, October 2008, and October 2010.
In total, there were nine peaks and troughs with rainfall outside the normal range. Six of them came in the spring months of September, October or November.
Peaks and troughs in rainfall at Manilla quite often come near times of La Niña and El Niño. These are events in the record of Pacific Ocean temperatures called ENSO (El Niño – Southern Oscillation). The ENSO record for the last 17 years is shown in the second graph.

Continue reading →

Manilla Dew Point leads ENSO by three months

Posted in 21st-century climate, Anomalies smoothed, Australian Climate, Climate Change, Manilla NSW by

Manilla dew point lags NINO3.4 sea surface temperature by 3 months.

(This material justifies a statement in the post “Predict weather from ENSO?”)

The graphs above are like those in two previous posts, but show how Manilla smoothed monthly dew point anomalies, like temperature anomalies and rainfall anomalies, relate to the El Niño-Southern Oscillation (ENSO).

High (El Niño) values of Sea Surface Temperature (NINO3.4) are shown here to relate to low humidity at Manilla, NSW. As humidity data, I estimate dew points daily at sunrise. Dew points, like Sea Surface Temperatures, are expressed in degrees celsius, but corresponding anomalies take the opposite sense. The first graph plots the Manilla dew point anomaly, given a  negative sign, and the NINO3.4 anomaly. To improve the match, I have lagged the Manilla dew points by three months. As an example, I have noted on the graph the match of Manilla’s November 2005 humidity peak with the La Nina ENSO peak of February 2006.

To the eye, the over-all match is better than in either the rainfall or the maximum temperature plots of earlier posts. The two curves here match very well from 2000 to 2007.

The second graph shows the discrepancy between the two curves. Dashed lines show limits of a good match at +/-0.5 degrees. The nature of each larger discrepancy is noted. (“Here” in text boxes means “at Manilla”.)
After 2007 there are large mis-matches between Manilla dew point and ENSO. Dew point fluctuations suddenly become less than might be expected from NINO3.4 values. It may be relevant that, as I posted elsewhere  in July 2010, skies suddenly became very much cloudier at Manilla after August 2007.

I have also tried plotting the following variables against NINO3.4:

Daily minimum temperature;
Daily temperature range;
Percent cloudy mornings;
Subsoil temperature.

None of them matches NINO3.4 well enough to display.

The three sets of graphs show “teleconnections” between Sea Surface Temperatures in the equatorial Pacific and climate variables at Manilla in inland NSW, Australia. Climatic peaks come earlier at Manilla than in the Pacific:

Peaks of daily maximum temperature come one month earlier;
peaks of rainfall come two months earlier;
peaks of Dew Point come three months earlier.

In a simple-minded way, it seems to me more likely that Australia’s climate drives the Southern Oscillation than the other way around. I know that this is speculation. (Sort of like Abraham Ortellius suggesting in 1587 that Africa and South America might have drifted apart.)

Notes
1. High frequency noise is reduced in the case of the Manilla monthly data by a Gaussian smoothing function of half-width six months.
2. On advice, I represent the El Nino – Southern Oscillation phenomenon (ENSO) by the NINO3.4 area anomalies from the OISSTv2 data set.
My enquiries about the best data to use are in this “weatherzone”  thread.
The ensemble of sea surface temperatures does not have much high-frequency noise. There is some, however, and I have used the same smoothing as used in the (formerly authoritative) Oceanic Nino Index (ONI), that is, a running mean of each three monthly values.

This was posted originally in a “weatherzone” forum, with the date 12 November 2011. It is posted here with the nominal date 29 November 2011.

 

Manilla rainfall extremes reflect NINO3.4 temperature

Posted in 21st-century climate, Anomalies smoothed, Australian Climate, Climate Change, Manilla NSW by

Manilla rainfall matches NINO3.4 sea surface temperature.

(This material justifies a statement in the post “Predict weather from ENSO?”)

The graphs above are like those in an earlier post, but show how Manilla monthly rainfall anomalies, rather than maximum temperature anomalies relate to the El Nino-Southern Oscillation (ENSO). Most people using ENSO  want to predict Australian regional rainfall.

In the second graph I have improved the match at peaks and troughs of smoothed Manilla monthly rainfall anomalies and NINO3.4 sea surface temperature anomaly data in two ways.
1. I converted the sea surface temperature anomaly (degrees C) into a model of resultant rainfall anomaly (mm) by multiplying by minus fifteen.
2. I added 3.7 mm of rainfall to the Manilla figures, and I lagged the data by two months.

To the eye, the over-all correspondence between actual and modelled rainfall is good, but not quite as good as in the temperature graphs. One form of mis-match is that two of the greatest rainfall deficits (“El Nino” Nov-06, Dec-09) are broader and shallower than in the model. (Perhaps an arithmetic measure of rainfall anomaly is not the best.)

The third graph shows how much Manilla rainfall, as adjusted, differs from the rainfall “predicted” by the NINO3.4 model. Dashed lines show limits of a good match at +/- 7.5 mm (corresponding to +/-0.5 degrees). The nature of each larger discrepancy is noted.

A good match demands lagging actual rainfall at Manilla by two months. That implies that peaks and troughs in Manilla rainfall anomalies happen two months before the matching anomalies of NINO3.4. I wonder if prediction is even practical if that is the case in other parts of Australia.

Notes
1. High frequency noise is reduced in the case of the Manilla monthly data by a Gaussian smoothing function of half-width six months.
2. On advice, I represent the El Nino – Southern Oscillation phenomenon (ENSO) by the NINO3.4 area anomalies from the OISSTv2 data set.
My enquiries about the best data to use are in this “weatherzone”  thread.
The ensemble of sea surface temperatures does not have much high-frequency noise. There is some, however, and I have used the same smoothing as used in the (formerly authoritative) Oceanic Nino Index (ONI), that is, a running mean of each three monthly values.

This was posted originally in a “weatherzone” forum, with the date 28 October 2011. It is posted here with the nominal date 16 November 2011.

(Note added: Updated to include 2013 here.)