21-C Rain ENSO IPO: Scatterplot

The anomaly of monthly rainfall at Manilla, NSW varied with that of ENSO for only a part of the 21st century to date.

Scatterplot Rainfall vs ENSO

This connected scatterplot relates the smoothed anomaly of the El Niño-Southern Oscillation (ENSO) to the smoothed and 2-month-lagged anomaly of monthly rainfall at Manilla, NSW. The earlier data, from September 1999 to September 2011, is plotted in blue, and the later data, from October 2011 to November 2018, in red.

The same data was displayed as a dual-axis line plot in an earlier post titled “21-C Rain-ENSO-IPO: Line graphs”. Data sources are linked there.

The line plot revealed two things: the relationship changed from earlier to later times, and there was a better match when the rainfall data was lagged by two months. To clarify, I prepared various scatterplots with fitted regressions.

Raw data scatterplots

Scatterplots of the raw data values yielded regressions with very low values of the coefficient of determination (R-squared). For the whole population, R-squared was 0.028. I then checked the coefficient when I lagged the rainfall by 1-, 2-, or 3-months. A 1-month lag almost doubled the coefficient to 0.041; a 2-month lag gave 0.055, and a 3-month lag gave 0.041 again.
My observation that Manilla rainfall typically leads ENSO by 2 months is confirmed.

Connected scatterplots of smoothed and lagged data

The smoothing function used in the dual-axis line plot of the earlier post makes a good visual match. That suggests that local rainfall and ENSO are physically related at a periodicity no shorter than 12 months.
Using the smoothed and 2-month-lagged data, I have made the scatterplots shown in the graph above.
The better-matched data from September 1999 to September 2011 (blue) has a satisfactory R-squared of 0.498, nearly 20 times greater than that of the raw data. The very poorly-matched data from October 2011 to November 2018 (red) has an R-squared value of 0.040, no better than the raw data.

Patterns in the sequence of rainfall and ENSO values

In the above graph, I have joined the consecutive smoothed data points to make a connected scatterplot. Because little noise remains, clear patterns appear.

Matched rainfall and ENSO

The pattern up to September 2011 (blue) is mainly a series of ellipses, some clockwise and some anti-clockwise. They are almost parallel to the regression line:

y = -0.047x-0.246

The blue point furthest to the top left is that for September 2002, a time of extreme drought and El Niño.
The blue point furthest to the bottom right is that for December 2010, a time of very high rainfall and La Niña.

Discordant rainfall and ENSO

The pattern from October 2011 (red) swings about wildly and does not repeat. The regression (with a trivial coefficient of determination) is nearly horizontal. Near its ends are the extreme drought of June 2018 and the deluge of January 2012, both at times when ENSO was near neutral. At the top of the graph is the Super El Niño of November 2015, when Manilla rainfall was normal.

Conclusion

Scatterplots, connected scatterplots and regressions confirm that a strong relation between rainfall at Manilla and ENSO failed in 2011 as the IPO was rising from a negative toward a positive regimen.

January “Coolth” in a House without Air-Conditioning

I have now 15 years of January average temperature data for my house at Manilla, North-west Slopes, NSW. These graphs show how the house temperature relates to the outdoor (or ambient) maximum, mean, and minimum temperatures.Regression graphs of indoor on outdoor temp in the hottest month

The house is not too hot and not too cold

Solar-Passive House from the NE.

House at Monash St Manilla from NE

In January (the hottest month) the rooms* in this solar-passive house do not heat up much during the day, nor do they cool down much at night. Since the indoor temperature always rises and falls just one or two degrees from the mean, only the mean is shown. Green lines on the graphs, which are drawn to pass through the middle of each cloud of data points, show by how much (on the average) the indoor temperatures have differed from the outdoor maximum, mean, and minimum temperatures. On the middle graph the green line shows that the rooms have been 0.5° cooler than the mean temperature outdoors. The left graph shows that the rooms have been 8.2° cooler than the daily maximum outdoor temperatures. The right graph shows that the rooms have been 7.3° warmer than the daily minimum overnight temperatures.

The design of the house aimed to protect those living there from excessive summer heat. It may seem that reducing the mean temperature by only half a degree is a failure. Not so! The January mean temperature at this site (26.1°) is near the middle of the adaptive comfort zone for this month, and so is the indoor mean temperature (25.6°). The house succeeds in keeping the indoor temperature comfortable in the heat of the day, when that outdoors is an uncomfortable 34 degrees. The high thermal mass that achieves this has the unfortunate result that the minimum indoor temperature overnight (not shown) is some five degrees warmer than the outdoor minimum. However, on average, it is still a comfortable 23.5 degrees. (Curiously, no-one knows the best room temperature for sleep.) Continue reading

July Warmth in an Unheated House

Solar-Passive House from the NW

House at Monash St Manilla from NW

I have fifteen years of temperature data for my high-mass, solar passive, unheated house at Manilla, NSW, Australia. This article has been posted previously here. These graphs show how July temperatures indoors relate to those outdoors. Indoor maxima and minima are not shown, because they are consistently between one and two degrees above and below the indoor mean.

House and ambient temperatures, 15 July months. The house is much warmer (dashed green lines)

In July, the rooms* in this solar-passive house, heated only by the sun, are much warmer than outdoors. This is shown by the green lines on the graphs, which are drawn to pass through the middle of each cloud of data points. The middle graph shows that, as an average over 15 July months, the rooms have been 8.7 degrees warmer than outdoors. The left graph shows that the rooms have even been 1.4 degrees warmer than the daily maximum outdoor temperatures. The right graph shows that the rooms have been nearly sixteen degrees warmer than the daily minimum overnight temperatures. To stay warm in this way the house must have absorbed many hundreds of kilowatt hours of heat from the sun. I have burned a few kilowatt hours of grid power to maintain my comfort, but this cannot have warmed the house by as much as one tenth of a degree in any month. Continue reading

Indoor/Outdoor Regressions for Maxima and Minima

Regressions for maximum and minimum temperatures compared

This graph shows the two regression lines for Indoor versus Outdoor daily maximum temperature (purple) and daily minimum temperature (green), taken from separate scatter-plots for maxima and minima. I have marked three points on each line: the mean temperature point and points at the extreme ends of the lines, one for a very hot day and one for a very cold day.

The interest of this graph is in the space between the regression lines. It represents the daily temperature range. I have linked each pair of points by two lines like the tread and riser of a stair. The tread (red) is the outdoor daily temperature range; the riser (blue) is the indoor daily temperature range.

The mean outdoor temperature range here is 15.4° and the mean indoor temperature range of the house is 3.1°. By this measure, the indoor temperature range is one fifth of that outdoors.
It happens that, in Manilla, the outdoor temperature ranges in the hottest and coldest parts of the year are, as shown, slightly less than for the year as a whole. Indoor temperature ranges show a clear gradient, from as much as 3.7° on a very hot day through 3.1° at the mean, to only 2.3° on a very cold day.

These very narrow temperature ranges result from the way the high thermal mass dispersed within the house allows heat to be absorbed and radiated at room temperature, eliminating extremes. Hot spots and cold spots are few and do not last long.

Adaptive Comfort

[I have re-posted the lost graph of the Adaptive Comfort Zone here.]

For comfort, we do not need indoor temperature ranges as narrow as these. Using the Adaptive Comfort Zone model we find that the neutrality temperature (for best comfort) based on Manilla’s January mean temperature of 26°  is also 26°, and the neutrality temperature based on Manilla’s July mean temperature of 10° is 21°.
According to the model, 80% of the population feel comfortable when the temperature is within 3.5° of the neutrality temperature: in January at Manilla they are comfortable up to 29.5°, and in July they are comfortable down to 17.5°.
My graph shows that the maximum indoor temperature of this house on a very hot day (29.9°)is only 0.4° above the January comfort limit, and the minimum indoor temperature on a very cold day (15.8°) is just 1.7° below the July comfort limit.
On this model, most people could live comfortably in this house using heating or cooling for only a few days in a year.
This post is one of a set of four back-dated to June 2010:
Indoor versus Outdoor Temperatures (1096 days)
Indoor versus Outdoor Minima (1096 days)
Indoor versus Outdoor Maxima (1096 days)
Indoor/Outdoor Regressions for Maxima and Minima (This post.)


This article was originally posted in the weatherzone forum thread “Indoor Climate” on 9th June 2010. It is backdated here to 19th June 2010.