[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.
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.
2 thoughts on “Manilla NSW in Global Warming Context”
Very interesting to see Manilla is behaving like the whole world. What is an “anomaly” in this context?
Hello again, Alison
Anomaly: a deviation from the normal or usual order. But you knew that!
But what is the normal or usual order when we are dealing with climate change? This question is at the core of the climate change discussion, but is difficult to pin down. Our record of (fairly) reliable data is so very short.
Because we know very little about the way that world temperature has varied over time, we use an absurdly simple model for what is “normal”: normally world temperature stays the same!
The Goddard Institute has chosen to use the mean temperature found in their data set for the period 1951 to 1980 to represent the normal temperature. What is plotted on the graph is the anomaly: the temperature estimate for a month, with the 1951-1980 mean temperature for that time of year subtracted. The Goddard Institute’s page of FAQ’s begins with the question “What are temperature anomalies?”
My own data set begins in April 1999, and I have no way of knowing exactly what the monthly mean temperatures were here from 1951 to 1980. I have chosen as my normal temperatures the monthly mean temperatures in the decade beginning April 1999. My temperature anomalies have those mean values subtracted. Consequently, the zero of the scale for my data differs from that for GISS.
It would be equally valid to take a constant rate of global warming to be “normal”, and to call deviations from that steadily increasing temperature to be the “anomaly”. (I have done it myself.)