Log of the Southern Oscillation Index with climate shifts

SOI plot with climate shifts

This graph relates to a graph of the cumulative values of the Southern Oscillation Index, posted earlier and copied below.

SOI CUSUM plotThe graph above is in a more familiar form . It may help to explain what the earlier graph means. That is, that the SOI was dominated by positive values (towards La Niña) for about fifty-nine years before 1976, and was dominated by negative values (towards El Niño) for twenty-four years after that date. From 2000 the trend seems to be upward, showing La Niña dominance again. Broadly, these were straight-line CUSUM  relationships throughout each of the periods, as shown by the coloured trend lines. Slopes on a CUSUM plot represent offsets of the mean monthly value: the mean SOI in the earlier period was +1.4 units, and that in the second period was -3.5 units. Since 2000, the mean monthly value is around +1.0 units. Continue reading

Manilla in Global Warming Context: II

Logs of smoothed world and local temperatures. (25/7/14)

This post updates a similar post that was based on data available in July 2011. I now have data from three more years.

World surface air temperature

The blue line shows how the air has warmed and cooled during the 21st century. It is based on GISS, which is one of three century-long records that estimate the surface air temperature of the whole earth. The other two are HadCRUT and NCDC.
Monthly values of GISS vary wildly, and I have smoothed them with a 37-month moving average. Ole Humlum uses 37-month smoothing in many graphs on his website.

The 37-month smoothing allows plotting only up to 18 months ago, in December 2012. As you see, the GISS air temperature anomaly (See Note 1.), when smoothed in this way, moves rather steadily in one direction for years at a time.

The world’s surface air warmed rapidly from early 2000 to late 2002, then warmed slowly to a peak in early 2006. This is the warmest the world surface air has been in hundreds of years. After that peak, the air cooled rapidly by two-thirtieths of a degree to a trough in late 2007. It warmed again slowly to a lower peak in early 2010, steadied for a year, then fell to a trough in January 2012 that was like the previous trough. The air warmed rapidly through 2012. Continue reading

Manilla NSW in Global Warming Context

Logs of smoothed world and local temperatures.

[I posted an Up-dated version of this graph in July 2014]

Up-to -date data on global temperature change can easily be down-loaded from Ole Humlum’s website “climate4you“.
Humlum favours sampling windows 37 months wide. For my own data at Manilla, NSW, I have always used windows about six months wide, which show up Australia’s vigorous Quasi-biennial oscillations of climate. I tried Humlum’s 37-month window on my data, with quite startling results, as shown in the graph above.

Humlum re-presents three records since 1979 of global monthly air surface temperature anomalies:
* HadCRUT3: by the (UK Met Office) Hadley Centre for Climate Prediction and Research, and the University of East Anglia’s Climatic Research Unit (CRU), UK.
* NCDC: National Climatic Data Centre, NOAA, USA.
* GISS: Goddard Institute for Space Studies, Columbia University, New York, NASA, USA.
When smoothed by a 37-month running average, these data sets give very similar results. I use the GISS data because it matches my data best.

The match is very good, particularly in the sharp fall from the maximum in April 2006 to the minimum in September 2007. Where my data begins in September 2000, both curves rise steeply from low values, but mine peaks in August 2001, more than a year before a corresponding peak in global temperature (September 2002). After that, there is a plateau, where the graphs rise together to the highest peak (April 2006).
The other global data sets, HadCRUT and NCDC, have temperature falling or steady along the 2002-2006 plateau.
There are two reasons for plotting my data on a separate axis (on the right). First, the reference periods are different: GISS uses 1951-1980, while I use the decade from April 1999. Second, temperature varies much more at a single station than in the average of many stations around the world. I use a scale six times larger.

It turns out that the cold time in Manilla in late 2007, which I had mentioned in several contexts, was a cold time world-wide.

Home-made thermometer screen

Giant Mixing Bowl Thermometer Screen

I am over the moon at getting agreement between data from my home-made thermometer screen and the best that world climatologists can do. It makes me inclined to believe some of the things they say.


This article and graph were posted on 18th August 2011 in a weatherzone forum: General Weather/ Observations of Climate Variation.

 

Manilla 30-year Monthly Rainfall Anomalies

Manilla 30-year Monthly Rainfall Anomalies

In an earlier post I modelled the seasonal distribution of rainfall at Manilla, NSW, as a bi-modal Gaussian distribution with a higher Gaussian peak very close to the summer solstice and a lower one very close to the winter solstice.
Monthly discrepancies of the 125-year average from the model are small. They are plotted in black on each of the two graphs here. Only two months could not be made to fit the model well: October has 6.2 mm more rain than expected, and December has 10.0 mm less.
The graphs show anomalies from the model for each of five “epochs” of three decades (or less). They are:
1883 to 1900 – “19th Century” (19thC)
1901 to 1930 – “World War I” (WW I)
1931 to 1960 – “World War II” (WW II)
1961 to 1990 – “BoM Normal Period” (BoM)
1991 to 2012 – “21st Century” (21stC)
Continue reading

A Seasonal Rainfall Model for Manilla, NSW

Model of seasonal rainfall, Manilla

At 31 degrees south latitude, Manilla, in eastern Australia, lies between the winter rainfall regime of the westerly belt and the summer regime of the monsoon. Much more rain falls at Manilla in summer than in winter.
On this graph, the rainfall distribution by calendar months is shown by the black line and numbers (mm) . This is the average curve for the 125-year period from the first observations in 1883 up to 2007. For any shorter period the curve is not smooth. This record is scarcely long enough to yield a stable estimate of the seasonal pattern. Continue reading