21-C Rain-ENSO-IPO: Line graphs

From 1999, rainfall at Manilla NSW matched ENSO only up to 2011, before the IPO became positive.

Manilla rain, ENSO, IPO

This graphical log compares the rainfall at Manilla NSW with the El Niño-Southern Oscillation (ENSO) and the Inter-decadal Pacific Oscillation (IPO) through the 21st century to date. Values shown are anomalies, smoothed. (See Notes below on “Data”, “Smoothing”, and “Lagged Rainfall”.)

Rainfall (black) uses the left axis scale; the ENSO (magenta) and the IPO (green) use the inverted right axis scale.

[21st century temperature and rainfall at Manilla are compared as smoothed data in the post “21-C Climate: Mackellar cycles”.]

Matches between rainfall and ENSO

There is an excellent match between the rainfall and ENSO values in the left part of the graph.
I improved the visual match by various means:
1. The ENSO scale (magenta) is inverted, because positive values of the ENSO anomaly relate to negative values of rainfall anomaly here.
2. The scales are harmonised: the zero values are aligned, and 20 mm of monthly rainfall anomaly is scaled to (minus) one degree of ENSO anomaly.
3. Smoothing is applied to suppress cycles shorter than 12 months.
4. Rainfall anomaly values are lagged by two months. (See the Note below.)
As lagged, most peaks and troughs of rainfall coincide with troughs and peaks of ENSO, and their sizes (as scaled) are often similar.

Failure to match rainfall and ENSO

In the right part of the graph, the match between rainfall and ENSO fails. There are extreme mismatches: the Super-El Niño of 2014-16 had no effect on local rainfall, the rainfall deluge of 2011-12 came with a mild and declining La Niña, and the extreme drought of 2018 came while ENSO was neutral.
By visual inspection, I judge that a close relation of rainfall to ENSO, which had applied for the twelve years up to September 2011, then failed for the following seven years.

Influence of the IPO

The inter-decadal Pacific Oscillation (IPO) affects the relation between ENSO and Australian weather. (See note below “Effect of the IPO”.)

Power et al.(1999) show that Australian seasonal weather and its prediction align with ENSO only when the IPO is negative. It follows that a good match between ENSO and Manilla rainfall was expected while the IPO (green) was negative from 1999 to 2013, and was not expected from 2014 to 2017. The trend of the IPO through 2016-17 makes it likely that the IPO continued positive through 2018, as the mismatch between rainfall and ENSO persisted.
Power et al. note that the relation is not sensitive to the width of a neutral zone chosen to separate the positive and negative regimens of the IPO. In this particular case, the rainfall/ENSO match failed as the IPO rose through minus one degrees. However, the rainfall/ENSO match began in 1999, much earlier than the time when the IPO fell through minus one degrees.

Scatter plots

In a following post I show scatter plots and regressions for the periods of match and mismatch on this graphical log.




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HadCRUT Global Temperature Smoothing

Graph of recent HadCRUT4

As a long-term instrumental record of global temperature, the HadCRUT4 series may be the best we have. [See Ole Humlum’s blog in the notes below.]
I like to use the published smoothed annual series of HadCRUT4.  I find that this smoothing gets rid of the “noise” that makes graphs about global warming needlessly hard to read. I used the smoothed HadCRUT series to point out the curious inverse relation between the rate of warming and the rate of growth of carbon emissions in this post from 2014.  I will refer again to that post in discussing the use of bent-line regression to describe global warming.

The Met Office Hadley Centre published the smoothing procedure that they used for the time series of smoothed annual average temperature in the HadCRUT3 data set. The smoothing function used is a 21-point binomial filter. The weights are specified in the link above.
The authors discuss the fudge that they use to plot smoothed values up to the current year, even though a validly smoothed value for that year would require ten years of data from future years. Their method is to continue the series by repeating the final value. They had added to the uncertainty by using a final value from just part of a year.
They relate how this procedure had caused consternation when the smoothed graph published in March 2008 showed a curve towards cooling, due to the final value used being very cool.
They show the effect by displaying the graph for that date.
They maintain that the unacceptable smoothed curve (because it shows cooling, not warming) is due mainly to using a final value from an incomplete year, saying:
“The way that we calculate the smoothed series has not changed except that we no longer use data for the current year in the calculation.”
That web-page is annotated:
“Last updated: 08/04/2008 Expires: 08/04/2009”
However, this appears to be the current procedure, used with the HadCRUT4 data set.

For my own interest, I plotted the values from 1990 to 2016 of the annual series of HadCRUT4, averaged over northern and southern hemispheres. [Data sources below.]

On my graph (above), all points 1990 to 2016 are as sourced. I have plotted raw values 2017 to 2026 (uncoloured) as I believe they are used in the smoothing procedure. I have also left uncoloured the smoothed data points from 2007 to 2016, to indicate that their values are not fully supported by data.

I agree with Ole Humlum that it is very good of the Met Office to come clean on the logical shortcomings of their procedure for smoothing, but it would be even better if they ceased plotting smoothed points when the smoothing depends on data points for future years.
In my monthly series of parametric plots of smoothed monthly values of climate anomaly variables, I have faced the same problem. I smooth using a 13-point Gaussian curve. My solution is to plot “fully-smoothed” data points (in colour) up to six months ago. That gives a consistent mapping up to that date. The fifth month before now (plotted uncoloured) is smoothed with an 11-point Gaussian and so on, up to the latest month with a necessarily unsmoothed value. A recent example of my parametric plots is “Hot and dry records in January 2019” .


Ole Humlum’s blog “Climate4you”

[See: Index\Global Temperature\Recent global air temperature change, an overview\]

HadCRUT4 data
Source of raw annual values:

Source of smoothed annual values:

[5 June 2019. Sorry, I see that both of those links yield “Not found.” Isn’t the internet wonderful!  That is the way of the future, folks. Every link will break, sooner rather than later.]

Profile of an Extreme Drought

Rainfall vs Maximum temperature, 2002 droughtAt Manilla, NSW, there was a drought in 2002 that was extreme, but brief. There have been no other extreme droughts at Manilla in the 21st century. The current drought is not as bad (yet).
The first graph shows a profile of the 2002 drought. Low rainfall is at the top, and hot days are on the right. Droughts, with low rainfall and hot days, will be near the top right corner. Normal climate is marked by a rectangle (coloured aqua (aqua)) in the middle.
The climate in these months moved into drought and out of it. January 2001 (Start) had perfectly normal climate with no drought, and so did February 2003 (Finish). Rainfall first became lower than normal after January 2002, and reached a minimum 27 mm below normal in July 2002. Rainfall returned to the normal range by December 2002. Day-time temperature went above the normal range in May 2002, reached a peak 1.3 degrees above normal in September-October 2002, and fell back into the normal range in January 2003. For rainfall lower than normal, the drought lasted ten months: for days hotter than normal, it lasted eight months. In this drought, the time of lowest rainfall came two to three months earlier than the time of hottest days.

(There is more detailed analysis of  the 2002 drought in a post dated September 2004.)

Graphs showing the progress of the drought as rainfall shortages are in the post “The 2002 rainfall shortages at Manilla”.

The loop on the graph shows this drought as a simple event with a beginning, a middle, and an end. Droughts are not usually seen to be so simple. This graph is made using two “tricks”: anomalies and smoothing. You must judge whether you trust them to describe the drought as it happened.

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