55. Austria - temperature trends STABLE to 1980

The temperature trend for Austria from 1780 to 1950 is qualitatively very similar to that of its neighbour Hungary over the same time period. In both cases the temperature declined from around 1800 to about 1880, and then rose again afterwards. Thus temperatures in the middle part of the 20th century were broadly similar to those in 1800. This can be seen most clearly by comparing the 5-year moving average for Austria in Fig. 55.1 below (yellow curve) with the equivalent curve for Hungary in Fig. 54.1 in Post 54. For this period the temperature trend is therefore stable but with considerable natural variation. This also suggests that the trend in Fig. 55.1 is accurate as it is in effect corroborated by the data from its neighbour, Hungary.

 

From 1951 to 1980 the mean temperature in Austria increases slightly. Then around 1988 there is a sudden jump in temperature of about 1 °C similar to that seen in Hungary, Czechoslovakia, the Baltic States, Germany and Denmark. In fact comparing the mean temperature for the period 1991-2010 with that for 1961-1980 indicates a sudden rise of 0.99 °C, while the average for 1991-2010 is 1.37 °C above the overall 1781-1950 trend (see the red best fit line in Fig. 55.1).

Fig. 55.1: The temperature trend for Austria since 1767. The best fit is applied to the interval 1781-1950 and has a positive gradient of +0.05 ± 0.08 °C per century. The monthly temperature changes are defined relative to the 1981-2010 monthly averages

The best fit line in Fig. 55.1 is calculated for the period 1781-1950. The reasons for this choice are statistical accuracy and impartiality. I could have chosen the period 1781-1980. This would have represented a full two centuries of data, but it would give a misleading value for the trend (of 0.20 ± 0.06 °C per century) because it would be calculated over a non-integer number of cycles in the natural variability. This variability peaks around 1780 and 1950. That is why the trend in Fig. 55.1 is calculated between those two dates. 

Fitting to the full length of the data also poses similar drawbacks. In addition, the anomaly data clearly shows a different form of behaviour after 1980 compared to before. It would therefore be inappropriate to analyse both time-frames with a single best fit line. For that reason I have restricted the linear regression analysis to the 1781-1950 interval. For more explanation of the rationale I have employed here I suggest referring to the discussion of Fig. 4.7 in Post 4, the discussion of Fig. 18.3 in Post 18, and the discussion of Fig. 30.3 in Post 30.

Fig. 55.2: The number of station records included each month in the mean temperature trend for Austria when the MRT interval is 1981-2010.

The anomalies used to calculate the trend in Fig. 55.1 were determined relative to the mean temperatures for the interval 1981-2010. This interval corresponds to a maximum in the number of available station records that had over 480 months of data (see Fig. 55.2 above) and therefore should lead to more accurate results for the trend. In all, 27 stations in Austria have over 480 months of data (see here), of which 26 have sufficient data in the MRT interval to qualify for inclusion in the overall trend. The exception is Obir (Berkeley Earth ID: 5111) which although having 1153 months of data has none after 1944. It is therefore excluded. For a detailed explanation of MRTs and their use in determining the temperature anomalies please refer to Post 47.

Of the 26 station records included in the trend in Fig. 55.1 fifteen had over 1200 months of data. The geographical locations of these long stations are shown on the map in Fig. 55.3 below. The remaining medium stations (with over 480 months of data) are also shown as small diamonds.

Fig. 55.3: The locations of long stations (large squares) and medium stations (small diamonds) in Austria. Those stations with a high warming trend are marked in red. Those with cooling or stable trends are marked in blue.

The map in Fig. 55.3 illustrates how evenly the long and medium stations in Austria are distributed across the country. This allows their anomalies to be averaged without any need for different weightings for different stations to be employed. This equal weighting approach was used to construct the trend in Fig. 55.1. I have also used it to construct a Berkeley Earth version based on their adjusted data. This is shown in Fig. 55.4 below.

Fig. 55.4: Temperature trend in Austria since 1767 derived by aggregating and averaging the Berkeley Earth adjusted data for all long and medium stations. The best fit linear trend line (in red) is for the period 1831-1980 and has a gradient of +0.36 ± 0.03 °C/century.

There are two things that are striking about the trend for the Berkeley Earth adjusted data in Fig. 55.4. Firstly, it agrees almost exactly with the trend published by Berkeley Earth and shown in Fig. 55.5 below even though the trend in Fig. 55.4 uses an equal station weighting approach while the trend in Fig, 55.5 does not. This suggests that adjusting the station weightings has a minimal effect on the result, and so validates the approach taken to calculate the trends in both Fig. 55.4, and more importantly Fig. 55.1. But secondly, much of the cooling between 1820 and 1900 is erased. The result is that the temperature trend for 1831-1980 is reduced from +0.73 ± 0.10 °C per century to a more modest +0.36 ± 0.03 °C per century. In other words, the trend looks more like the IPCC hockey stick.

Fig. 55.5: The temperature trend for Austria since 1750 according to Berkeley Earth.

The difference in the trends from 1831-1980 for the data in Fig. 55.1 and Fig. 55.4 is the result of adjustments made to the data by Berkeley Earth. These adjustments are commonplace in the Berkeley Earth data and have been documented in many of my previous posts, but usually they tend to increase the trend compared to that seen for the raw data. In this case, though, these adjustments actually reduce the trend between 1831 and 1980. However, before 1831 the adjustments effectively add warming to the trend by reducing temperatures before 1830. The net effect of these two sets of adjustments is to flatten the curve between 1770 and 1980 and make it appear more like a hockey stick. These adjustments are shown in Fig. 55.6 below.

Fig. 55.6: The contribution of Berkeley Earth (BE) adjustments to the anomaly data in Fig. 55.4 after smoothing with a 12-month moving average. The blue curve represents the total BE adjustments including those from homogenization. The linear best fit (red line) to these adjustments for the period 1841-2010 has a negative gradient of -0.394 ± 0.004 °C per century. The orange curve shows the contribution just from breakpoint adjustments.

Conclusions

1) The temperature trend for Austria is qualitatively very similar to that of its neighbour Hungary. This effectively allows the trends from these two adjacent countries to corroborate each other's results. 

2) The temperature trend for Austria is stable up until 1980. The net upward trend of 0.4 °C is comparable to the natural variation. 

3) After 1980 there is a sudden increase in temperature of about 1 °C that occurs around 1988. The reason for this is unknown.

30. Temperature trends in Antarctica - VARIABLE

If there is one region of the planet that is synonymous with climate change, it is probably Antarctica. Climate change, we are told, is melting the ice cap, the glaciers, the ice shelves and the sea ice. As a result penguins may become extinct in 100 years. Or not, because it turns out there are actually a lot more of them than we thought. So what is really happening in Antarctica?

Well, the honest answer is that we don't really know.  Despite being one of the most studied places on the planet, there is virtually no instrumental temperature data from before 1940. The continent has over 260 instrumental temperature records, but most are less than 40 years in length. In fact only about 56 have more than 240 months of data, of a mere 22 have more than 480 months of data. As the following analysis will show, this is insufficient to draw any accurate or definitive conclusions about the current temperature trends for the continent.

Fig. 30.1: A map of Antarctica showing the locations of all the stations with temperature records containing more than 240 months of data.

Part of the problem with analysing the temperature records of Antarctic is the sheer size of the place. It has almost twice the area of Australia, but the weather stations are not evenly distributed. And given its size, it would be inappropriate to simply aggregate trends from opposite sides of the continent, for the same reasons as for Australia; principally, that they are likely to be totally uncorrelated. When looking at the spatial distribution of stations it becomes clear that most are situated on the coast (see Fig. 30.1 above). Those that are inland are usually at altitude, and as I showed in Post 4, the temperatures in the interior of Antarctica are much lower than elsewhere, and have much higher levels of variability. This implies that they should be analysed and aggregated separately.

In addition, the coastal stations appear to exist in three distinct clusters. The most obvious two are the high densities of stations on the peninsula and around the Ross Sea. In contrast, the stations around the Atlantic coast from longitude 45° W to 90° E are more evenly spread. It therefore seems logical to subdivide the stations into four separate groupings: (i) those found on the Antarctic Peninsula; (ii) the interior stations at altitude; (iii) the stations located along the Pacific coast from the Amundsen Sea in the east, to Queen Mary Land in the west via the Ross Sea; (iv) the stations on the Atlantic Coast from 45° W to 90° E. These four groupings of stations are identified in Fig. 30.1 above. 

 

Fig. 30.2: Number of stations active each month that have more than 240 months of data overall.

 

In Post 4 I looked at the three most significant station records for the interior of Antarctica: Amundsen-Scott Base (Berkeley ID - 166900), Vostok (Berkeley ID - 151513) and Byrd Station (Berkeley ID - 166906). The data for Byrd Station was fragmented, while that for both Amundsen-Scott and Vostok indicated negative temperature trends. No other stations in the interior have more than 240 months of data.

Using 240 months as the cutoff, we find that the number of active stations in the other three regions of Antartica that contain this minimum amount of monthly temperature data never exceeds 20, and in the case of the Atlantic coast, it never exceeds 10 (see Fig. 30.2 above). In addition, most of the data is concentrated from 1980 onwards, and only the Antarctic Peninsula has any data before 1950, but even that is miniscule in terms of its total amount.

Fig. 30.3: The mean temperature for the Pacific coast of Antarctica since 1950. The best fit line is fitted to data from 1973-2010 and has an overall trend of 0.55 ± 0.80 °C per century.

If we calculate the mean temperature trend using the data that is available, the results are not great, at least not if you are a firm believer in climate change. The data for the Pacific coast displays a small amount of warming of 0.55 °C per century since 1973 as shown in Fig. 30.3 above (i.e. 0.21 °C in total). The period 1973-2010 was chosen for the best fit calculation because that time-frame is bounded by two peaks in the 5-year moving average. This means that the peaks do not distort the best fit calculation for reasons that I have outlined in the discussion of Fig. 4.7 in Post 4. 

If the best fit in Fig. 30.3 were to be made to all the data, then best fit trend becomes 1.61 °C per century. The dip around 1960 now pulls down the trend line and increases the warming trend, but is this localized dip in the temperature record permanent or just temporary? The answer is that we don't know because there is insufficient data before 1960 to judge.

 

Fig. 30.4: The mean temperature for the Atlantic coast of Antarctica since 1950. The best fit line is fitted to data from 1973-2010 and has an overall negative trend of -0.21 ± 0.60 °C per century.

If we now turn to the Atlantic coast the pattern is the same. The temperature trend is relatively stable from 1970 to 2010 (see Fig. 30.4). If we measure the trend for 1973-2010 in order to compare directly with that for the Pacific coast, we see that the trend is actually slightly negative and equal to -0.21 ± 0.60 °C per century. But again, extending the fitting to all the data changes the trend to a positive one of gradient +0.49 ± 0.31 °C per century. This is, once again a consequence of a dip in temperatures around 1960. This suggests that the temperature fall is real, and not due to measurement errors, but this dip is large enough to completely change the trend from -0.21 °C per century to +0.49 °C per century.

There is one other similarity with the Pacific coast data: the uncertainties in both trends are very large. This is due to the comparatively short time frame for the available data, which illustrates why long temperature records are so valuable. Even 60 years is not long enough.

Fig. 30.5: The mean temperature for the Antarctic Peninsula since 1940. The best fit line is fitted to data from 1973-2010 and has an overall trend of 2.88 ± 0.77 °C per century.

The notable point about the Antarctic Peninsula is that it is the only region of Antarctica where there is clear evidence of a significant warming trend since 1950. But this is no different from what we have seen in Australia and New Zealand, and in this case there is no data before 1940. That means we cannot say whether this warming is new and permanent, or whether, like Australia and New Zealand, it is just a recovery from a temporary cooling phase. In Australia and New Zealand the temperatures in the latter half of the 19th century were just as high as they are now. In the case of Antarctica we just do not know.

Summary

The analysis above allows us to draw the following conclusions.

1. There has been no warming trend in the interior of Antarctica since 1957 (see Post 4).
   
2. The has been no warming trend on the Atlantic coast since 1950, and probably none of any great consequence on the Pacific coast either (see Fig. 30.4 and Fig. 30.3).
3. The only significant recent warming in Antarctic appears to be around the peninsula (as shown in Fig. 30.5). This warming is, however, no greater than that seen in Australia and New Zealand over the same time period (1950-2010), and that warming was preceded by a cooling of almost equal magnitude (see Post 26 and Post 8).
4. We have no idea what the temperature trend anywhere in Antarctica was before 1940.
   

4. Data analysis at the South Pole

If there is one place on Earth that is synonymous with global warming, it is Antarctica. The conventional narrative is that because of climate change, the polar ice caps are melting, all the polar bears and penguins are being rendered homeless and are likely to drown, and the rest of the planet will succumb to a flood of biblical proportions that will turn most of the Pacific islands into the Lost City of Atlantis, and generally lead to global apocalypse. Needless to say, most of this is a gross exaggeration.

I have already explained that melting sea ice at the North Pole cannot raise sea levels because of Archimedes’ principle. The same is true of ice shelves around Antarctica. The only ice that can melt and raise sea levels is that which is on land. In Antarctica (and Greenland) this is virtually all at altitude (above 1000 m) where the mean temperature is below -20 °C, and the mean monthly temperature NEVER gets above zero, even in summer. Consequently, the likelihood of any of this ice melting is negligible.

The problem with analysing climate change in Antarctica is that there is very little data. If you exclude the coastal regions and only look at the interior, there are only twenty sets of temperature data with more than 120 months of data, and only four extend back beyond 1985. Of those four, one has 140 data points and only runs between 1972 and 1986 and so is nigh on useless for our purposes. The other three I shall consider here in detail.

The record that is the longest (in terms of data points), most complete and most reliable is the one that is actually at the South Pole. It is at the Amundsen-Scott Base that is run by the US government and has been permanently manned since 1957. The graph below (Fig. 4.1) illustrates the mean monthly temperatures since 1957.

Fig. 4.1: The measured monthly temperatures at Amundsen-Scott Base.

The thing that strikes you first about the data is the large range of temperatures, an almost 40 degree swing from the warmest months to the coldest. This is mainly due to the seasonal variation between summer and winter. Unfortunately, this seasonal variation makes it virtually impossible to detect a discernible trend in the underlying data. This is a problem that is true for most temperature records, but is acutely so here. However, there is a solution. If we calculate the mean temperature for each of the twelve months individually, and then subtract these monthly means from all the respective monthly temperatures in the original record, what will be left will be a signal representing time dependent changes in the local climate.

Fig. 4.2: The monthly reference temperatures (MRTs) for Amundsen-Scott Base.

The graph above (Fig. 4.2) illustrates the monthly means for the data in Fig. 4.1. We get this repeating data set by adding together all the January data in Fig. 4.1 and dividing it by the number of January readings (i.e. 57). Then we repeat the method for the remaining 11 months. Then we plot the twelve values for each year to give a repeating trend as illustrated in Fig. 4.2. If we then subtract this data from the data in Fig. 4.1 we get the data shown below (Fig. 4.3). This is the temperature anomaly for each month, namely the amount by which the average temperature for that month has deviated from the expected long-term value shown in Fig. 4.2. This is the temperature data that climate scientists are interested in and try to analyse. The monthly means in Fig. 4.2 therefore represent a series of monthly reference temperatures (MRTs) that are subtracted to the raw data in order to generate the temperature anomaly data. The temperature anomalies are therefore the amount by which the actual temperature each month changes relative to the reference or average for that month.

Fig. 4.3: The monthly temperature anomalies for Amundsen-Scott Base.

Also shown in Fig. 4.3 is the line of best fit to the temperature anomaly (red line). This is almost perfectly flat, although its slope is slightly negative (-0.003 °C/century). Even though the error in the gradient is ±0.6 °C per century, we can still venture, based on this data that there is no global warming at the South Pole.

The reasons for the error in the best fit gradient being so large (it is comparable to the global trend claimed by the IPCC and climate scientists) are the large temperature anomaly (standard deviation = ±2.4 °C) and the relatively short time baseline of 57 years (1957-2013). This is why long time series are essential, but unfortunately these are also very rare.

Then there is another problem: outliers. Occasionally the data is bad or untrustworthy. This is often manifested as a data-point that is not only not following the trend of the other data, it is not even in the same ballpark. This can be seen in the data below (Fig. 4.4) for the Vostok station that is located over 1280 km from the South Pole.

Fig. 4.4: The measured monthly temperatures at Vostok.

There is clearly an extreme value for the January 1984 reading. There are also others, including at March 1985 and March 1997, but these are obscured by the large spread of the data. They only become apparent when the anomaly is calculated, but we can remove these data points in order to make the data more robust. To do this the following process was performed.

First, find the monthly reference temperaturs (MRTs) and the anomalies as before. Then, calculate the mean anomaly. Next, calculate either the standard deviation of the anomalies, or the mean deviation (either will do). Then I set a limit for the maximum number of multiples of the deviation that an anomaly data point can lie above or below the mean value for it to be considered a good data point (I generally choose a factor of 5). Any data-points that fall outside this limit are then excluded. Then, with this modified dataset, I recalculated the MRTs and the anomalies once more. The result of this process for Vostok is shown below together with the best fit line (red line) to the resulting anomaly data (Fig. 4.5).

Fig. 4.5: The monthly temperature anomalies for Vostok.

Notice how the best fit line is now sloping up slightly, indicating a warming trend. The gradient, although looking very shallow, is still an impressive +1.00 ± 0.63 °C/century, which is more than that claimed globally by the IPCC for the entire planet. This shows how difficult these measurements are, and how statistically unreliable. Also, look at the uncertainty or error of ±0.63 °C/century. This is almost as much as the measured value. Why? Well, partly because of the short time baseline and high noise level as discussed previously, and partly because of the underlying oscillations in the data which appear to have a periodicity of about 15 years. The impact of these oscillations becomes apparent when we reduce or change the length of the base timeline.

Fig. 4.6: The monthly temperature anomalies for Vostok with reduced fitting range.

In Fig. 4.6 the same data is presented, but the best fit line has only been performed to data between 1960 and 2000. The result is that the best fit trend line (red line) changes sign and now demonstrates long-term cooling of -0.53 ± 1.00 °C/century. Not only has the trend changed sign, but the uncertainty has increased.

What this shows is the difficulty of doing a least squares best fit to an oscillatory dataset. Many people assume that the best fit line for a sine wave lies along the x-axis because there are equal numbers of points above and below the best fit line. But this is not so, as the graph below illustrates.

 Fig. 4.7: The best fit to a sine wave.

The best fit line to a single sine wave oscillation of width 2π and amplitude A is 3A/π² (see Fig. 4.7). This reduces by a factor n for n complete oscillations but it never goes to zero. Only a best fit to a cosine wave will have zero gradient because it is symmetric. Yet the problem with temperature data is that most station records contain an oscillatory component that distorts the overall trend in the manner described above. This is certainly a problem for many of the fits to shorter data sets (less than 20 years). But a far bigger problem is that most temperature records are fragmented and incomplete, as the next example will illustrate.

Fig. 4.8: The measured monthly temperatures at Byrd Station.

Byrd Station is located 1110 km from the South Pole. Its local climate is slightly warmer than those at Amundsen-Scott and Vostok but the variation in seasonal temperature is just as extreme (see Fig. 4.8 above). Unfortunately, its data is far from complete. This means that its best fit line is severely compromised.

Fig. 4.9: The monthly temperature anomalies for Byrd Station.

The best fit to the Byrd Station data has a warming trend of +3.96 ± 0.83 °C/century (see the red line in Fig. 4.9 above). However, things are not quite that simple, particularly given the missing data between 1970 and 1980 which may well consist of a data peak, as well as the sparse data between 2000 and 2010 which appears to coincide with a trough. It therefore seems likely that the gradient would be very different, and much lower, if all data were present. How much lower we will never know. Nor can we know for certain why so much data is missing. Is this because the site of the weather station changed? In which case, can we really consider all the data to being part of a single record, or should we be analysing the fragments separately? This is a major and very controversial topic in climate science. As I will show later, it leads to the development of controversial numerical methods such as breakpoint alignment and homogenization.

What this post has illustrated I hope, is the difficulty of discerning an unambiguous warming (or cooling) trend in a temperature record. This is compounded by factors such as inadequate record length, high noise levels in signals, missing and fragmented data, and underlying nonlinear trends of unknown origin. However, if we can combine records, could that improve the situation? And if we do, would it yield something similar to the legendary hockey stick graph that is so iconic and controversial in climate science? Next I will use the temperature data from New Zealand to try and do just that.