From 1999, rainfall at Manilla NSW matched ENSO only up to 2011, before the IPO became positive.
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.
In a following post I show scatter plots and regressions for the periods of match and mismatch on this graphical log.
Monthly rainfall totals from 1883 to 26/3/2015 are from the manual record of Manilla Post Office, Station 055031.
That station reported automatically from 23/5/16 to 7/10/16. Renamed Manilla (Museum) Station 055312, it reported from 17/3/17 to 24/9/17, from 16/3/18 to 30/8/18, and from 20/7/19 to date (10/8/19). Most readings after March 2015 (27 of 43 months) are from my own gauge.
The rainfall anomaly is the monthly rainfall total less the 125-year mean total for that calendar month.
The El Niño-Southern Oscillation (ENSO) data are the anomaly values of the Optimum Interpolated Sea Surface Temperature (OISST.v2) in the region NINO3.4. [For ENSO, I do not use the alternative lower-resolution data set: the Extended Reconstructed Sea Surface Temperature ERSSTv5.]
Data are those published by the Climate Prediction Center (CPC) of the National Centers for Environmental Prediction (NCEP) of the National Oceanic and Atmospheric Administration (NOAA) of the U.S.Department of Commerce (DOC), as indexed at this site.
My data source page is this one.
I use the right-hand column, the NINO3.4 anomaly.
When accessed, the latest data was for July 2019.
Data for the Inter-decadal Pacific Oscillation is published by the Center for Ocean-Land-Atmosphere studies (COLA) at George Mason University (GMU) Virginia USA at this URL.
In that page, titled “Climate of the 20th century”, go to Experiments\IPO Time series. That is a short article by Chris Folland: “Interdecadal Pacific Oscillation Time series”. It introduces two tables of the IPO. Table 2 is raw monthly data. I use Table 1, which is data filtered using an 11-year Chebyshev filter, and expressed as averages for the four seasons of each year, beginning with “JFM”. I have assigned the “JFM” value to February, and so on.
Smoothing uses a Gaussian function with a Standard Deviation of 2.5 months. It spans 13 monthly data points, and has a half-width of 6 months, which suppresses cycles shorter than 12 months.
Extremes in Manilla rainfall generally occur earlier than those of ENSO (as specified here). In the graph below, nearly every peak or trough in rainfall (black arrow) is to the left of a corresponding tough or peak in ENSO (magenta arrow).
Typically, the rainfall leads by two months. The amount of lead is considered further in a following post.
In the graph at the top of this post, Rainfall data has been lagged by two months for the best match.
Effect of the IPO
The effect of the IPO on Australian weather is the topic of the following paper:
S. Power, T. Casey, C. Folland, A. Colman, V. Mehta (1999): “Inter-decadal modulation of the impact of ENSO on Australia”, Climate Dynamics 15: 319-324.
From the caption to Fig. 3 on p. 321 of that paper:
“Thus when the IPO index is <-0.05 the frequency of both poor performance (negative skill scores) and low skill (small positive scores) is reduced and the frequency of skilful performance (high positive skill scores) is increased. Results are not especially sensitive to this choice of threshold (i.e. 0.05).”
[Note added 20 August 2019.
See also Henley et al. (2015).
Benjamin J. Henley, Joelle Gergis, David J. Karoly, Scott Power, John Kennedy, Chris K. Folland (2015). “A Tripole Index for the Interdecadal Pacific Oscillation”, Clim Dyn DOI 10.1007/s00382-015-2525-1]
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