New Zealand is, however, surprising in one sense: despite being a small country with an even smaller population, it has the second highest number of long temperature records in the Southern Hemisphere. Only Australia has more station records with more than 1200 monthly measurements each (1200 being the equivalent of more than 100 years of data). New Zealand therefore seems like a good place to start analysing regional temperature trends.
According to Berkeley Earth, New Zealand has about 64 station records (it may be slightly more or less depending on whether you include some near to Antartica or some South Pacific Islands). Of these, ten have more than 1200 months of temperature data stretching back to the 19th Century, including two that date back to January 1853. I shall characterize these as long stations due to the length of their records. In addition there are a further 27 stations with more than 240 months of data which could be characterized as medium length stations. This includes a further dozen or so stations that contain data covering most of the period from 1973 to 2013.
In my previous post I explained how temperature data can be processed into a usable form comprising the temperature anomaly, and how these anomalies can be combined to produce a global warming trend for the region (see Eq. 5.11 here). This process involves combining multiple temperature records from the same country or region into a single numerical series by adding the anomaly data from the same month in each record and taking the average. This new average should, in theory, have less noise that the individual records from which it is constructed because the averaging process should lead to a regression towards the mean. What is left should be a general trend curve that consists of the signal of long-term climate change for that region together with a depreciated noise component. As a starting point we shall in the next post look at combining the ten longest data sets for New Zealand and seeing how the warming trend it produces compares with the trend as advertised by climate scientists.
As I noted last time, we need to be careful in regard to how the different records are combined, and in particular, to consider two main issues. The first is the evenness of the distribution of the stations across the region in question. If stations are too close together they will merely reproduce each other’s data and render one or more of them redundant. Ideally they should be evenly spaced, otherwise they should be weighted by area (see Eq. 5.15 here).
Fig. 6.1: Geographical distribution of long (1200+ months), medium (400+ months) and short (240+ months) temperature records in New Zealand.
If we look at the spatial distribution of the long stations in New Zealand (see Fig. 6.1.), we see that they are indeed distributed very evenly across the country. This means that weighting coefficients are unnecessary and the local warming trend can be approximated to high precision merely by adding the anomalies from each station record.
The second issue regards the construction of the temperature anomalies themselves (how these anomalies are derived has been discussed here). These anomalies are the amount by which the average temperature for a particular month in a given year has deviated from the expected long-term value for that month. In other words, by how much does the average temperature for this month (May 2020) differ from the average temperature for all months of May over the last 30 years or so? Central to this derivation is the construction of a set of monthly average temperatures, which involves finding the mean temperature for each of the 12 months over a pre-defining time interval of about 30 years as outlined here and in Fig. 4.2 here. I call these averages the monthly reference temperatures (MRTs) because they are temperatures against which the actual data is compared in order to determine the monthly change in temperature. These temperature changes or anomalies are in essence a series of random fluctuations about the mean value, but they may also exhibit an underlying trend over time. It is this trend that climate scientists are seeking to identify and measure.
This immediately raises an important question: over what period should the reference temperature for each month be measured? Most climate science groups seem to favour a thirty year period from 1961-1990. It appears that this is chosen because it tends to correspond to a period with a high number of active stations, and this is certainly true for New Zealand. As the Berkeley Earth graph in Fig. 6.2 below shows, the number of active stations in New Zealand has risen over time, peaking at over 30 in the last few decades. However, when it comes to finding the optimum period for the MRT calculation, relying on station population frequencies is not always the best illustrator.
Fig. 6.2: New Zealand stations used in the Berkeley Earth average.
What we really require is a time period which allows us to incorporate the maximum number of data points into our analysis. This can be achieved, not by summing the number of active stations each month, but instead by summing the total number of data points that each of the stations present in that month possesses. Such a graph of the sum of station frequency x data length versus time is shown below in Fig. 6.3.
Fig. 6.3: Data frequency over time.
Fig. 6.3 shows more clearly that the period 1970-2000 is the Goldilocks zone for calculating the MRT. Choosing this time period for the MRT not only allows us to incorporate a large number of stations, but it also means we will have a large number of data points per temperature record, and hence a longer trend. Nevertheless, not all temperature records will have enough data in this region, and some useful data could still be lost. So why not choose a different period, say 10 years, or a longer period, say 50 years or 100 years that could access the lost data? And how much effect would this choice make on the overall warming trend?
The problem is that there are various competing drivers at play here. One is the need to have the longest temperature record, as that will yield the most detectable temperature trend. But measurement accuracy also depends on having the highest number of stations in the calculation, and in having an accurate determination of the MRT for each. And of course, ideally the same time-frame should be used for all the different temperature records that are to be combined in order to maintain data consistency and accuracy. Unfortunately, this is not always possible as most temperature records tend to have different time-frames. When faced with the need to compromise, it is generally best to try different options and seek the optimal one.
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